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\n \n 2020\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Dynamic Bayesian network for crop growth prediction in greenhouses.\n \n \n \n \n\n\n \n Kocian, A.; Massa, D.; Cannazzaro, S.; Incrocci, L.; Di Lonardo, S.; Milazzo, P.; and Chessa, S.\n\n\n \n\n\n\n Computers and Electronics in Agriculture, 169: 105167. 2 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Dynamic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Dynamic Bayesian network for crop growth prediction in greenhouses},\n type = {article},\n year = {2020},\n pages = {105167},\n volume = {169},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0168169919321131},\n month = {2},\n id = {e67d8640-f1d5-387c-b5ce-07c87ef676e6},\n created = {2020-01-11T19:54:57.509Z},\n accessed = {2020-01-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2020-01-11T19:54:57.509Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Kocian, A. and Massa, D. and Cannazzaro, S. and Incrocci, L. and Di Lonardo, S. and Milazzo, P. and Chessa, S.},\n doi = {10.1016/j.compag.2019.105167},\n journal = {Computers and Electronics in Agriculture}\n}

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\n \n 2019\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model.\n \n \n \n \n\n\n \n Manyweathers, J.; Maru, Y.; Hayes, L.; Loechel, B.; Kruger, H.; Mankad, A.; Xie, G.; Woodgate, R.; and Hernandez-Jover, M.\n\n\n \n\n\n\n Preventive Veterinary Medicine,104872. 12 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Understanding$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model},\n type = {article},\n year = {2019},\n pages = {104872},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0167587719306348},\n month = {12},\n id = {32f376b2-c8e7-3f13-8bca-647d1255567d},\n created = {2019-12-21T18:28:26.511Z},\n accessed = {2019-12-21},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2019-12-21T18:28:26.589Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Manyweathers, Jennifer and Maru, Yiheyis and Hayes, Lynne and Loechel, Barton and Kruger, Heleen and Mankad, Aditi and Xie, Gang and Woodgate, Rob and Hernandez-Jover, Marta},\n doi = {10.1016/j.prevetmed.2019.104872},\n journal = {Preventive Veterinary Medicine}\n}

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\n \n 2018\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Effect of a Synthetic Feline Pheromone for Managing Unwanted Scratching.\n \n \n \n \n\n\n \n Beck, A.; De Jaeger, X.; Collin, J.; and Tynes, V.\n\n\n \n\n\n\n Intern J Appl Res Vet Med, 16(1). 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Effect$ Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@article{\n title = {Effect of a Synthetic Feline Pheromone for Managing Unwanted Scratching},\n type = {article},\n year = {2018},\n volume = {16},\n websites = {http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf},\n id = {70b5431d-86e2-32cd-a3ca-c8bae461407a},\n created = {2018-04-07T13:44:32.818Z},\n accessed = {2018-04-07},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2018-04-07T13:44:32.818Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Objectives The scratching of objects in the environment is a normal part of the feline behavioural repertoire, but it appears to be one of the more disturbing problems reported by cat owners. In fact, even in the presence of a scratching post, a large majority of owners still observe scratching on unwanted loca-tions in the home. Methods The present study tested a solution contain-ing a synthetic analogue of a pheromone -the feline interdigital semiochemical -to determine if it was sufficiently attractive to redirect any scratching behaviour to a scratching post. Cat owners facing unwanted scratching in the home were instructed to follow a protocol consisting of the appli-cation of this pheromone directly on the scratching post. Results We demonstrated that 74% of the cats with established unwanted scratching completely stopped scratching on vertical surfaces in the home, other than the treated scratching post after 28 days of application. Moreover this treatment also decreases scratching on horizontal surfaces in these cats. This treat-ment also appears to have a preventative effect when applied in homes with a recently adopted cat. Conclusion In summary, the application of a synthetic analogue of the feline interdigital phero-mone appears to be an innovative and ef-fective solution to overcome the frequent behavioural scratching problem in cats.},\n bibtype = {article},\n author = {Beck, A. and De Jaeger, X. and Collin, J.-F. and Tynes, V.},\n journal = {Intern J Appl Res Vet Med},\n number = {1}\n}

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\n \n 2015\n \n \n (4)\n \n \n

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\n \n\n \n \n \n \n \n \n Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species.\n \n \n \n \n\n\n \n Fernandes, J., A.; Irigoien, X.; Lozano, J., A.; Inza, I.; Goikoetxea, N.; and Pérez, A.\n\n\n \n\n\n\n Ecological Informatics, 25: 35-42. 1 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluating$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species},\n type = {article},\n year = {2015},\n keywords = {Bayesian networks,Fisheries management,Kernel density estimation,Pelagic fish,Recruitment forecasting,Supervised classification},\n pages = {35-42},\n volume = {25},\n websites = {http://www.sciencedirect.com/science/article/pii/S1574954114001563},\n month = {1},\n id = {f73c004a-7bc8-363f-b95f-548427d4783f},\n created = {2015-04-11T19:51:57.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.},\n bibtype = {article},\n author = {Fernandes, Jose A. and Irigoien, Xabier and Lozano, Jose A. and Inza, Iñaki and Goikoetxea, Nerea and Pérez, Aritz},\n doi = {10.1016/j.ecoinf.2014.11.004},\n journal = {Ecological Informatics}\n}

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\n\n\n\n\n\n

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\n \n\n \n \n \n \n \n \n Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: An application to riparian buffer strips.\n \n \n \n \n\n\n \n McVittie, A.; Norton, L.; Martin-Ortega, J.; Siameti, I.; Glenk, K.; and Aalders, I.\n\n\n \n\n\n\n Ecological Economics, 110: 15-27. 2 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Operationalizing$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: An application to riparian buffer strips},\n type = {article},\n year = {2015},\n keywords = {Bayesian networks,Ecosystem services,Interdisciplinary research,Valuation},\n pages = {15-27},\n volume = {110},\n websites = {http://www.sciencedirect.com/science/article/pii/S0921800914003711},\n month = {2},\n id = {a4ae39cb-6633-35ec-9b0f-b84ffa33bfdf},\n created = {2015-04-11T19:51:57.000Z},\n accessed = {2015-01-21},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The interface between terrestrial and aquatic ecosystems contributes to the provision of key ecosystem services including improved water quality and reduced flood risk. We develop an ecological–economic model using a Bayesian Belief Network (BBN) to assess and value the delivery of ecosystem services from riparian buffer strips. By capturing the interactions underlying ecosystem processes and the delivery of services we aim to further the operationalization of ecosystem services approaches. The model is developed through outlining the underlying ecological processes which deliver ecosystem services. Alternative management options and regional locations are used for sensitivity analysis. We identify optimal management options but reveal relatively small differences between impacts of different management options. We discuss key issues raised as a result of the probabilistic nature of the BBN model. Uncertainty over outcomes has implications for the approach to valuation particularly where preferences might exhibit non-linearities or thresholds. The interaction between probabilistic outcomes and the statistical nature of valuation estimates suggests the need for further exploration of sensitivity in such models. Although the BBN is a promising participatory decision support tool, there remains a need to understand the trade-off between realism, precision and the benefits of developing joint understanding of the decision context.},\n bibtype = {article},\n author = {McVittie, Alistair and Norton, Lisa and Martin-Ortega, Julia and Siameti, Ioanna and Glenk, Klaus and Aalders, Inge},\n doi = {10.1016/j.ecolecon.2014.12.004},\n journal = {Ecological Economics}\n}

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\n \n\n \n \n \n \n \n \n Quantifying the determinants of outbreak detection performance through simulation and machine learning.\n \n \n \n \n\n\n \n Jafarpour, N.; Izadi, M.; Precup, D.; and Buckeridge, D., L.\n\n\n \n\n\n\n Journal of biomedical informatics, 53: 180-7. 2 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Quantifying$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Quantifying the determinants of outbreak detection performance through simulation and machine learning.},\n type = {article},\n year = {2015},\n keywords = {Bayesian networks,Disease outbreak detection,Outbreak simulation,Predicting performance,Public health informatics,Surveillance},\n pages = {180-7},\n volume = {53},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046414002299},\n month = {2},\n id = {9109ebeb-a2db-379d-b1e1-8de4f35964e3},\n created = {2015-04-11T19:52:22.000Z},\n accessed = {2015-03-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {OBJECTIVE: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.\n\nMATERIALS AND METHODS: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.\n\nRESULTS: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.\n\nCONCLUSION: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.},\n bibtype = {article},\n author = {Jafarpour, Nastaran and Izadi, Masoumeh and Precup, Doina and Buckeridge, David L},\n doi = {10.1016/j.jbi.2014.10.009},\n journal = {Journal of biomedical informatics}\n}

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\n \n\n \n \n \n \n \n \n Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions.\n \n \n \n \n\n\n \n Allwood, J., W.; Chandra, S.; Xu, Y.; Dunn, W., B.; Correa, E.; Hopkins, L.; Goodacre, R.; Tobin, A., K.; and Bowsher, C., G.\n\n\n \n\n\n\n Phytochemistry. 2 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Profiling$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions.},\n type = {article},\n year = {2015},\n keywords = {Bayesian network analysis,Leaves,Metabolite fingerprinting,Metabolite profiling,Nitrate,Triticum aestivum,Wheat},\n websites = {http://www.sciencedirect.com/science/article/pii/S0031942215000424},\n month = {2},\n day = {10},\n id = {3fa56b17-da76-322f-87f1-3402de441048},\n created = {2015-04-11T20:33:13.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The control and interaction between nitrogen and carbon assimilatory pathways is essential in both photosynthetic and non-photosynthetic tissue in order to support metabolic processes without compromising growth. Physiological differences between the basal and mature region of wheat (Triticum aestivum) primary leaves confirmed that there was a change from heterotrophic to autotrophic metabolism. Fourier Transform Infrared (FT-IR) spectroscopy confirmed the suitability and phenotypic reproducibility of the leaf growth conditions. Principal Component-Discriminant Function Analysis (PC-DFA) revealed distinct clustering between base, and tip sections of the developing wheat leaf, and from plants grown in the presence or absence of nitrate. Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf. The operation of nitrogen metabolism impacted on the levels and distribution of amino acids, organic acids and carbohydrates within the wheat leaf. In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover. The value of using this combined metabolomics analysis for further functional investigations in the future are discussed.},\n bibtype = {article},\n author = {Allwood, J William and Chandra, Surya and Xu, Yun and Dunn, Warwick B and Correa, Elon and Hopkins, Laura and Goodacre, Royston and Tobin, Alyson K and Bowsher, Caroline G},\n doi = {10.1016/j.phytochem.2015.01.007},\n journal = {Phytochemistry}\n}

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\n \n 2014\n \n \n (8)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production.\n \n \n \n \n\n\n \n Ge, L.; van Asseldonk, M., A.; Valeeva, N., I.; Hennen, W., H.; and Bergevoet, R., H.\n\n\n \n\n\n\n NJAS - Wageningen Journal of Life Sciences, 70-71: 1-8. 12 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production},\n type = {article},\n year = {2014},\n keywords = {Animal health status,Bayesian belief network (BBN),Factor analysis,Management quality,Veterinary Antibiotic Use},\n pages = {1-8},\n volume = {70-71},\n websites = {http://www.sciencedirect.com/science/article/pii/S1573521414000025},\n month = {12},\n id = {b282a48d-0362-35be-872d-60c902fa88c7},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about potential causal factors of antibiotic use. Animal health status and management quality were considered the two most important factors that influence farmers’ decision-making concerning antibiotic use. The objective of this paper was to develop a Bayesian belief network (BBN) to analyze how these factors can directly and indirectly influence antibiotic use. Since both factors are not directly observable (i.e., latent), they were inferred from related observable variables (i.e., manifest variables). Using farm accounting data and registration data on antibiotic use and veterinary services in specialized finisher pig farms over the period 2007-2010, a confirmatory factor analysis was carried out to construct the two latent factors. Antibiotic use is quantified as the number of days per year in which an average pig is treated with antibiotics according to their standard daily dosages (NDD). Descriptive analysis on the data revealed that for the finisher pig farms, NDD averaged about 17 days, with substantial year-to-year variations and between-farm variations within the same year.The conditional probabilities for the BBN model were obtained through regression analysis between the constructed factors, NDD, and a number of technical and economic variables. The BBN model showed that antibiotic use was simultaneously influenced by the two latent factors, but in varying degrees depending on other variables. Therefore interventions targeting only to improve one factor are likely to lead to unsatisfactory outcomes of antibiotic use.},\n bibtype = {article},\n author = {Ge, Lan and van Asseldonk, Marcel A.P.M. and Valeeva, Natalia I. and Hennen, Wil H.G.J. and Bergevoet, Ron H.M.},\n doi = {10.1016/j.njas.2014.01.001},\n journal = {NJAS - Wageningen Journal of Life Sciences}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features.\n \n \n \n \n\n\n \n Gieder, K., D.; Karpanty, S., M.; Fraser, J., D.; Catlin, D., H.; Gutierrez, B., T.; Plant, N., G.; Turecek, A., M.; and Robert Thieler, E.\n\n\n \n\n\n\n Ecological Modelling, 276: 38-50. 3 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},\n type = {article},\n year = {2014},\n keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},\n pages = {38-50},\n volume = {276},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380014000398},\n month = {3},\n id = {29074d6d-0549-3ccc-9d6d-eca9644117a9},\n created = {2015-04-11T19:51:57.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.},\n bibtype = {article},\n author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},\n doi = {10.1016/j.ecolmodel.2014.01.005},\n journal = {Ecological Modelling}\n}

\n

\n\n\n

\n Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modeling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model's dataset. We found that model predictions were more successful when the ranges of physical conditions included in model development were varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modeling impacts of sea-level rise or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n Ecological Modelling, 291: 42-57. 11 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Conservation,Land use planning,Land use suitability,Natural resource management,Stakeholder engagement},\n pages = {42-57},\n volume = {291},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380014003056},\n month = {11},\n id = {0be3d50d-3e5c-3085-a1e3-e8a070476433},\n created = {2015-04-11T19:51:57.000Z},\n accessed = {2015-01-27},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Land use change results from frequent, independent actions by decision-makers working in isolation, often with a focus on a single land use. In order to develop integrated land use policies that encourage sustainable outcomes, scientists and practitioners must understand the specific drivers of land use change across mixed land use types and ownerships, and must consider the combined influences of biophysical, economic, and social factors that affect land use decisions. In this analysis of two large watersheds covering a total of 1.9 million hectares in Maine, USA, we co-developed with groups of stakeholders land use suitability models that integrated four land uses: economic development, ecosystem protection, forestry, and agriculture. We elicited stakeholder knowledge to: (1) identify generalized drivers of land use change; (2) construct Bayesian network models of suitability for each of the four land uses based on site-level factors that affect land use decisions; and (3) identify thresholds of suitability for each factor and give relative weights to each factor. We then applied 12 distinct Bayesian models using 99 spatially explicit, empirical socio-economic and biophysical datasets to predict spatially the suitability for each of our four land uses on a 30m×30m pixel basis across 1.9 million hectares. We evaluated both the stakeholder engagement process and the land use suitability maps. Results demonstrated the potential efficacy of these models for strategic land use planning, but also revealed that trade-offs occur when stakeholder knowledge is used to augment limited empirical data. First, stakeholder-derived Bayesian land use models can provide decision-makers with relevant insights about the factors affecting land use change. Unfortunately, these models are not easily validated for predictive purposes. Second, integrating stakeholders throughout different phases of the modeling process provides a flexible framework for developing localized or generalizable land use models depending on the scope of stakeholder knowledge and available empirical data. The potential downside is that this can lead to more complex models than anticipated. The trade-offs between model rigor and relevance suggest an adaptive management approach to modeling is needed to improve the integration of stakeholder knowledge into robust land use models.},\n bibtype = {article},\n author = {},\n doi = {10.1016/j.ecolmodel.2014.06.023},\n journal = {Ecological Modelling}\n}

\n

\n\n\n

\n Land use change results from frequent, independent actions by decision-makers working in isolation, often with a focus on a single land use. In order to develop integrated land use policies that encourage sustainable outcomes, scientists and practitioners must understand the specific drivers of land use change across mixed land use types and ownerships, and must consider the combined influences of biophysical, economic, and social factors that affect land use decisions. In this analysis of two large watersheds covering a total of 1.9 million hectares in Maine, USA, we co-developed with groups of stakeholders land use suitability models that integrated four land uses: economic development, ecosystem protection, forestry, and agriculture. We elicited stakeholder knowledge to: (1) identify generalized drivers of land use change; (2) construct Bayesian network models of suitability for each of the four land uses based on site-level factors that affect land use decisions; and (3) identify thresholds of suitability for each factor and give relative weights to each factor. We then applied 12 distinct Bayesian models using 99 spatially explicit, empirical socio-economic and biophysical datasets to predict spatially the suitability for each of our four land uses on a 30m×30m pixel basis across 1.9 million hectares. We evaluated both the stakeholder engagement process and the land use suitability maps. Results demonstrated the potential efficacy of these models for strategic land use planning, but also revealed that trade-offs occur when stakeholder knowledge is used to augment limited empirical data. First, stakeholder-derived Bayesian land use models can provide decision-makers with relevant insights about the factors affecting land use change. Unfortunately, these models are not easily validated for predictive purposes. Second, integrating stakeholders throughout different phases of the modeling process provides a flexible framework for developing localized or generalizable land use models depending on the scope of stakeholder knowledge and available empirical data. The potential downside is that this can lead to more complex models than anticipated. The trade-offs between model rigor and relevance suggest an adaptive management approach to modeling is needed to improve the integration of stakeholder knowledge into robust land use models.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Computational Systems Biology.\n \n \n \n \n\n\n \n Kriete, A.; Eils, R.; Imoto, S.; Matsuno, H.; and Miyano, S.\n\n\n \n\n\n\n Elsevier, 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Computational$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@book{\n title = {Computational Systems Biology},\n type = {book},\n year = {2014},\n source = {Computational Systems Biology},\n keywords = {Bayesian network,Brute force,Circadian rhythms,Gene network,Greedy algorithm,Microarray,Petri net,Promoter regions},\n pages = {89-112},\n websites = {http://www.sciencedirect.com/science/article/pii/B978012405926900006X},\n publisher = {Elsevier},\n id = {61986292-4436-32b6-95fc-0f63bd3d96a3},\n created = {2015-04-11T19:52:01.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This chapter describes the computational methods for estimating, modeling, and simulating biological systems. It also presents two approaches to understand biological systems and describes a method and a software tool developed by our research group. Bayesian network is a mathematical model for representing causal relationships among random variables by using conditional probabilities. The conditional probabilities describe the parent-child relationships and can be viewed as an extension of the deterministic models like Boolean networks. This model is suited for modeling qualitative relations between genes and allows mathematical and algorithmic analyses. We also devised a method to infer a gene network in terms of a linear system of differential equations from time-course gene expression data. A software tool is developed based on Petri net to modeling and simulation of gene networks. With this software tool, various models have been constructed and its utility has been demonstrated in practice.},\n bibtype = {book},\n author = {Kriete, Andres and Eils, Roland and Imoto, Seiya and Matsuno, Hiroshi and Miyano, Satoru},\n doi = {10.1016/B978-0-12-405926-9.00006-X}\n}

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\n \n\n \n \n \n \n \n \n Methods in Biomedical Informatics.\n \n \n \n \n\n\n \n Sarkar, I., N.; Chang, H.; and Alterovitz, G.\n\n\n \n\n\n\n Elsevier, 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Methods$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@book{\n title = {Methods in Biomedical Informatics},\n type = {book},\n year = {2014},\n source = {Methods in Biomedical Informatics},\n keywords = {Bayesian inference,Bayesian networks,Bayesian statistics},\n pages = {241-264},\n websites = {http://www.sciencedirect.com/science/article/pii/B9780124016781000087},\n publisher = {Elsevier},\n id = {73d40548-7305-3334-a764-3cd12d5d5335},\n created = {2015-04-11T19:52:02.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Modern technologies can collect biomedical data at much greater resolution than ever before, leading to large amounts of data being generated from a single experiment. To extract crucial information from big datasets requires reliable reasoning techniques, one of which is Bayesian analysis. This chapter introduces Bayesian approaches to biomedical data analysis. We begin with the definition of Bayes’ theorem, which lays the foundation of all Bayesian methods. Model selection and parameter estimation are then discussed, followed by how Bayesian networks framework can be used to infer the interlinking dependence relations among variables. The chapter concludes with a discussion of how Bayesian approaches can be used in biomedical contexts.},\n bibtype = {book},\n author = {Sarkar, Indra Neil and Chang, Hsun-Hsien and Alterovitz, Gil},\n doi = {10.1016/B978-0-12-401678-1.00008-7}\n}

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\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Improved maximum likelihood reconstruction of complex multi-generational pedigrees.\n \n \n \n \n\n\n \n Sheehan, N., A.; Bartlett, M.; and Cussens, J.\n\n\n \n\n\n\n Theoretical Population Biology, 97: 11-19. 11 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Improved$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Improved maximum likelihood reconstruction of complex multi-generational pedigrees},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Constrained optimisation,Genetic marker data,Integer linear program},\n pages = {11-19},\n volume = {97},\n websites = {http://www.sciencedirect.com/science/article/pii/S0040580914000513},\n month = {11},\n id = {6d540d4e-1886-34a3-ae4a-9a3574c3e59c},\n created = {2015-04-11T19:52:12.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The reconstruction of pedigrees from genetic marker data is relevant to a wide range of applications. Likelihood-based approaches aim to find the pedigree structure that gives the highest probability to the observed data. Existing methods either entail an exhaustive search and are hence restricted to small numbers of individuals, or they take a more heuristic approach and deliver a solution that will probably have high likelihood but is not guaranteed to be optimal. By encoding the pedigree learning problem as an integer linear program we can exploit efficient optimisation algorithms to construct pedigrees guaranteed to have maximal likelihood for the standard situation where we have complete marker data at unlinked loci and segregation of genes from parents to offspring is Mendelian. Previous work demonstrated efficient reconstruction of pedigrees of up to about 100 individuals. The modified method that we present here is not so restricted: we demonstrate its applicability with simulated data on a real human pedigree structure of over 1600 individuals. It also compares well with a very competitive approximate approach in terms of solving time and accuracy. In addition to identifying a maximum likelihood pedigree, we can obtain any number of pedigrees in decreasing order of likelihood. This is useful for assessing the uncertainty of a maximum likelihood solution and permits model averaging over high likelihood pedigrees when this would be appropriate. More importantly, when the solution is not unique, as will often be the case for large pedigrees, it enables investigation into the properties of maximum likelihood pedigree estimates which has not been possible up to now. Crucially, we also have a means of assessing the behaviour of other approximate approaches which all aim to find a maximum likelihood solution. Our approach hence allows us to properly address the question of whether a reasonably high likelihood solution that is easy to obtain is practically as useful as a guaranteed maximum likelihood solution. The efficiency of our method on such large problems bodes well for extensions beyond the standard setting where some pedigree members may be latent, genotypes may be measured with error and markers may be linked.},\n bibtype = {article},\n author = {Sheehan, Nuala A. and Bartlett, Mark and Cussens, James},\n doi = {10.1016/j.tpb.2014.07.002},\n journal = {Theoretical Population Biology}\n}

\n

\n\n\n

\n The reconstruction of pedigrees from genetic marker data is relevant to a wide range of applications. Likelihood-based approaches aim to find the pedigree structure that gives the highest probability to the observed data. Existing methods either entail an exhaustive search and are hence restricted to small numbers of individuals, or they take a more heuristic approach and deliver a solution that will probably have high likelihood but is not guaranteed to be optimal. By encoding the pedigree learning problem as an integer linear program we can exploit efficient optimisation algorithms to construct pedigrees guaranteed to have maximal likelihood for the standard situation where we have complete marker data at unlinked loci and segregation of genes from parents to offspring is Mendelian. Previous work demonstrated efficient reconstruction of pedigrees of up to about 100 individuals. The modified method that we present here is not so restricted: we demonstrate its applicability with simulated data on a real human pedigree structure of over 1600 individuals. It also compares well with a very competitive approximate approach in terms of solving time and accuracy. In addition to identifying a maximum likelihood pedigree, we can obtain any number of pedigrees in decreasing order of likelihood. This is useful for assessing the uncertainty of a maximum likelihood solution and permits model averaging over high likelihood pedigrees when this would be appropriate. More importantly, when the solution is not unique, as will often be the case for large pedigrees, it enables investigation into the properties of maximum likelihood pedigree estimates which has not been possible up to now. Crucially, we also have a means of assessing the behaviour of other approximate approaches which all aim to find a maximum likelihood solution. Our approach hence allows us to properly address the question of whether a reasonably high likelihood solution that is easy to obtain is practically as useful as a guaranteed maximum likelihood solution. The efficiency of our method on such large problems bodes well for extensions beyond the standard setting where some pedigree members may be latent, genotypes may be measured with error and markers may be linked.\n

\n\n\n

\n \n\n \n \n \n \n \n \n A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination.\n \n \n \n \n\n\n \n Nielsen, J., D.; Salmerón, A.; and Gámez, J., A.\n\n\n \n\n\n\n International Journal of Approximate Reasoning, 55(1): 74-83. 1 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination},\n type = {article},\n year = {2014},\n keywords = {Bayesian networks,Decision support systems,Inference,Learning,Vegetal genetic improvement},\n pages = {74-83},\n volume = {55},\n websites = {http://www.sciencedirect.com/science/article/pii/S0888613X13000704},\n month = {1},\n id = {45879508-ac26-30f2-81e8-87034dba34a2},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper we describe a system designed for assisting geneticists in vegetal genetic improvement tasks. The system is based on the use of Bayesian networks. It has been developed under the industrial demands emerging from the area of Campo de Dalías in Almería (Spain), and is therefore oriented to producing new tomato varieties, which constitute the main product in the area. The paper concentrates on the main aspects of the design of the system.},\n bibtype = {article},\n author = {Nielsen, Jens D. and Salmerón, Antonio and Gámez, José A.},\n doi = {10.1016/j.ijar.2013.03.010},\n journal = {International Journal of Approximate Reasoning},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features.\n \n \n \n \n\n\n \n Gieder, K., D.; Karpanty, S., M.; Fraser, J., D.; Catlin, D., H.; Gutierrez, B., T.; Plant, N., G.; Turecek, A., M.; and Robert Thieler, E.\n\n\n \n\n\n\n Ecological Modelling, 276: 38-50. 3 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features},\n type = {article},\n year = {2014},\n keywords = {Bayesian network,Development,Habitat,Piping plover,Sea-level rise,Shorebird},\n pages = {38-50},\n volume = {276},\n websites = {https://darchive.mblwhoilibrary.org/handle/1912/7233},\n month = {3},\n publisher = {Elsevier},\n day = {31},\n id = {c93984b3-3602-380c-8000-7822d107c564},\n created = {2015-05-07T19:12:09.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n language = {en_US},\n private_publication = {false},\n abstract = {© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Modelling 276 (2014): 38–50, doi:10.1016/j.ecolmodel.2014.01.005.},\n bibtype = {article},\n author = {Gieder, Katherina D. and Karpanty, Sarah M. and Fraser, James D. and Catlin, Daniel H. and Gutierrez, Benjamin T. and Plant, Nathaniel G. and Turecek, Aaron M. and Robert Thieler, E.},\n doi = {10.1016/j.ecolmodel.2014.01.005},\n journal = {Ecological Modelling}\n}

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\n \n 2013\n \n \n (9)\n \n \n

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\n \n\n \n \n \n \n \n \n A predictive diagnostic model for wild sheep (Ovis orientalis) habitat suitability in Iran.\n \n \n \n \n\n\n \n Bashari, H.; and Hemami, M.\n\n\n \n\n\n\n Journal for Nature Conservation, 21(5): 319-325. 10 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A predictive diagnostic model for wild sheep (Ovis orientalis) habitat suitability in Iran},\n type = {article},\n year = {2013},\n keywords = {Bayesian Belief Networks,Decision support tool,Habitat suitability,Ovis orientalis,Wild sheep},\n pages = {319-325},\n volume = {21},\n websites = {http://www.sciencedirect.com/science/article/pii/S1617138113000460},\n month = {10},\n id = {ddf90443-dadc-3786-b5ca-e1757e4fa4eb},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Wild sheep (Ovis orientalis) as the true ancestor of domestic sheep (Ovis aries) is currently listed as vulnerable (VU) by IUCN. Effective conservation of this species requires collecting all available ecological knowledge into a single framework that could be used for management decision making. Use of Bayesian Belief Networks for such purposes have been advocated over recent decades as this approach can integrate different sources of knowledge and perform comprehensive analyses. We built a decision support tool using BBN to assist in habitat management of wild sheep populations throughout the species’ geographical range. The behaviour of the model was tested using scenario and sensitivity analysis. Habitat security, food and water suitability, and thermal cover were recognised as the main habitat variables determining wild sheep habitat suitability. Integrating the complex interactions between variables, the model can be applied for both diagnostic and predictive analyses answering “what if” and “how” questions. This approach can assist wildlife conservationists for building similar models for other poorly-studied threatened species.},\n bibtype = {article},\n author = {Bashari, Hossein and Hemami, Mahmoud-Reza},\n doi = {10.1016/j.jnc.2013.03.005},\n journal = {Journal for Nature Conservation},\n number = {5}\n}

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\n \n\n \n \n \n \n \n \n Mining monitored data for decision-making with a Bayesian network model.\n \n \n \n \n\n\n \n Williams, B.; and Cole, B.\n\n\n \n\n\n\n Ecological Modelling, 249: 26-36. 1 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Mining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Mining monitored data for decision-making with a Bayesian network model},\n type = {article},\n year = {2013},\n keywords = {Bayesian networks,Cyanobacteria,Data mining,Elicitation,Reservoir management,Water quality},\n pages = {26-36},\n volume = {249},\n websites = {http://www.sciencedirect.com/science/article/pii/S030438001200333X},\n month = {1},\n id = {08064040-90ac-3105-963f-6ca3243366d3},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-08},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A Bayesian network model of Anabaena blooms in Grahamstown Dam, near Newcastle, Australia is described. This model meets the criteria of being decision-focused, data driven, transparent, and capable of being used by non-expert modellers. Monitored data were arranged in a consistently formatted database from which the model could ‘learn’ probabilistic relationships between model elements such as pumped nutrient load, lake water column nutrient concentrations, and Anabaena concentrations. This ‘minimal model’ produced useful insights into ecosystem relationships and provided a basic model for later development. Subsequent modelling and elicitation of conditional probabilities from experts strengthened components of the model for which there is little data available. The approach to incorporating elicited data is described and some simple scenario testing is also presented. Management outcomes resulting from application of the model are presented.},\n bibtype = {article},\n author = {Williams, B.J. and Cole, B.},\n doi = {10.1016/j.ecolmodel.2012.07.008},\n journal = {Ecological Modelling}\n}

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\n \n\n \n \n \n \n \n \n Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment).\n \n \n \n \n\n\n \n Keshtkar, A.; Salajegheh, A.; Sadoddin, A.; and Allan, M.\n\n\n \n\n\n\n Ecological Modelling, 268: 48-54. 10 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Application$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment)},\n type = {article},\n year = {2013},\n keywords = {Bayesian network,Decision support,Integrated catchment management,Iran,Sustainability assessment,Vegetation management},\n pages = {48-54},\n volume = {268},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380013003967},\n month = {10},\n id = {970c5423-6c6f-3325-8f78-b3775f230ae6},\n created = {2015-04-11T19:51:57.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Catchment management is a process which increases the sustainable development and management of all catchment resources in order to maximize the balance among socioeconomic welfare and the sustainability of vital ecosystems. The increase of anthropogenic activities within river catchments causes degradation and serious problems for stakeholders and managers, particularly in arid and semi-arid regions. Although there are many techniques for solving these problems, it is not easy for catchment managers to apply them. An integrated Bayesian network model framework was applied to evaluate the sustainability of a semi-arid river catchment located in the Iranian Central Plateau river basin encompassing 32.6km2 area on the Hablehrood river catchment, located in the northern part of the Iranian Central Plateau river basin. The research illustrated the assessment of the relevant management problems, the model framework, and the techniques applied to extract input data. Results for the study area implementation and a suggestion for management are described and discussed.},\n bibtype = {article},\n author = {Keshtkar, A.R. and Salajegheh, A. and Sadoddin, A. and Allan, M.G.},\n doi = {10.1016/j.ecolmodel.2013.08.003},\n journal = {Ecological Modelling}\n}

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\n \n\n \n \n \n \n \n \n Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring.\n \n \n \n \n\n\n \n Bulu, H.; Alpkocak, A.; and Balci, P.\n\n\n \n\n\n\n Computers in biology and medicine, 43(4): 301-11. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Uncertainty$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring.},\n type = {article},\n year = {2013},\n keywords = {Algorithms,Bayes Theorem,Breast,Breast Neoplasms,Breast Neoplasms: diagnosis,Breast: pathology,Calcinosis,Calcinosis: pathology,Computer Simulation,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Female,Humans,Mammography,Mammography: methods,Models, Statistical,Programming Languages,Uncertainty,User-Computer Interface},\n pages = {301-11},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482513000139},\n month = {5},\n id = {9ab0ef84-7482-3400-8d65-84ddd26c9a47},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper presents an ontology-based annotation system and BI-RADS (Breast Imaging Reporting and Data System) score reasoning with Semantic Web technologies in mammography. The annotation system is based on the Mammography Annotation Ontology (MAO) where the BI-RADS score reasoning works. However, ontologies are based on crisp logic and they cannot handle uncertainty. Consequently, we propose a Bayesian-based approach to model uncertainty in mammography ontology and make reasoning possible using BI-RADS scores with SQWRL (Semantic Query-enhanced Web Rule Language). First, we give general information about our system and present details of mammography annotation ontology, its main concepts and relationships. Then, we express uncertainty in mammography and present approaches to handle uncertainty issues. System is evaluated with a manually annotated dataset DEMS (Dokuz Eylul University Mammography Set) and DDSM (Digital Database for Screening Mammography). We give the result of experimentations in terms of accuracy, sensitivity, precision and uncertainty level measures.},\n bibtype = {article},\n author = {Bulu, Hakan and Alpkocak, Adil and Balci, Pinar},\n doi = {10.1016/j.compbiomed.2013.01.001},\n journal = {Computers in biology and medicine},\n number = {4}\n}

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\n \n\n \n \n \n \n \n \n A Bayesian network model for predicting pregnancy after in vitro fertilization.\n \n \n \n \n\n\n \n Corani, G.; Magli, C.; Giusti, A.; Gianaroli, L.; and Gambardella, L., M.\n\n\n \n\n\n\n Computers in biology and medicine, 43(11): 1783-92. 11 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A Bayesian network model for predicting pregnancy after in vitro fertilization.},\n type = {article},\n year = {2013},\n keywords = {Adult,Algorithms,Area Under Curve,Bayes Theorem,Computer Simulation,Embryo Transfer,Embryo Transfer: statistics & numerical data,Embryo, Mammalian,Female,Fertilization in Vitro,Fertilization in Vitro: statistics & numerical dat,Humans,Pregnancy,Pregnancy: statistics & numerical data},\n pages = {1783-92},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482513002187},\n month = {11},\n id = {4158f0b6-856f-3f4e-80ed-d392e3a4c852},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set. Moreover, we assess by means of a simulation study the effectiveness of the model in supporting the selection of the embryos to be transferred.},\n bibtype = {article},\n author = {Corani, G and Magli, C and Giusti, A and Gianaroli, L and Gambardella, L M},\n doi = {10.1016/j.compbiomed.2013.07.035},\n journal = {Computers in biology and medicine},\n number = {11}\n}

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\n \n\n \n \n \n \n \n \n Ecological Networks in an Agricultural World.\n \n \n \n \n\n\n \n Tixier, P.; Peyrard, N.; Aubertot, J.; Gaba, S.; Radoszycki, J.; Caron-Lormier, G.; Vinatier, F.; Mollot, G.; and Sabbadin, R.\n\n\n \n\n\n\n Volume 49 of Advances in Ecological ResearchElsevier, 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Ecological$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@book{\n title = {Ecological Networks in an Agricultural World},\n type = {book},\n year = {2013},\n source = {Advances in Ecological Research},\n keywords = {Bayesian networks,Decision tools,Ecosystem services,Ecosystem services trade-off,Food web model,Markov decision process,Multi-scale management,Pest control},\n pages = {437-480},\n volume = {49},\n websites = {http://www.sciencedirect.com/science/article/pii/B978012420002900007X},\n publisher = {Elsevier},\n series = {Advances in Ecological Research},\n id = {78763a6f-44e4-33bc-a000-4bb0481793a6},\n created = {2015-04-11T19:52:11.000Z},\n accessed = {2014-11-19},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The development of new methods and approaches for ensuring the sustainability of agriculture and ecosystem services is an important challenge that ecologists, agronomists, and theoreticians must address together. Enhancement of ecosystem services needs to be addressed at different scales and should include the interaction between farmland biodiversity and stakeholders (farmers, managers, policy makers, etc.) to optimize service delivery. Predictions require an understanding of the interactions between numerous management options and components of biodiversity. Here, we argue that interaction networks on a broad sense (from food webs to landscapes networks in which nodes could be species, trophic groups, fields or farms) can help address this high level of complexity. We examine how tools from mathematics and artificial intelligence, developed for network modelling and reasoning, could be useful for assessing and enhancing ecosystems services. In doing this we highlight the gaps that currently exist between our questions about ecosystem service provision and our ability to answer them with current modelling approaches. We illustrate the use of these tools with three case studies related to ‘pest regulation services’. These include food web approaches to assess animal pest regulation services and decisional models to address management strategies for diseases and weeds. Finally, we describe how different types of network models might operate at different scales of management. The future challenge for agroecologists will be to produce models of interactions and emergent ecosystem services, which are sufficiently quantified and validated. We suggest that network ecology is a nascent research topic that is developing a strong and unified empirical and theoretical foundation, which could serve as the central paradigm for a sustainable, intensive agriculture in the future.},\n bibtype = {book},\n author = {Tixier, Philippe and Peyrard, Nathalie and Aubertot, Jean-Noël and Gaba, Sabrina and Radoszycki, Julia and Caron-Lormier, Geoffrey and Vinatier, Fabrice and Mollot, Grégory and Sabbadin, Régis},\n doi = {10.1016/B978-0-12-420002-9.00007-X}\n}

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\n \n\n \n \n \n \n \n \n Use of a Bayesian network model to identify factors associated with the presence of the tick Ornithodoros erraticus on pig farms in southern Portugal.\n \n \n \n \n\n\n \n Wilson, A., J.; Ribeiro, R.; and Boinas, F.\n\n\n \n\n\n\n Preventive veterinary medicine, 110(1): 45-53. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Use$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Use of a Bayesian network model to identify factors associated with the presence of the tick Ornithodoros erraticus on pig farms in southern Portugal.},\n type = {article},\n year = {2013},\n keywords = {Animal Husbandry,Animal Husbandry: methods,Animals,Bayes Theorem,Environment,Models, Biological,Ornithodoros,Ornithodoros: physiology,Portugal,Swine,Swine Diseases,Swine Diseases: epidemiology,Swine Diseases: parasitology,Tick Infestations,Tick Infestations: epidemiology,Tick Infestations: parasitology,Tick Infestations: veterinary},\n pages = {45-53},\n volume = {110},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587713000378},\n month = {5},\n day = {15},\n id = {7811f88a-31fa-3665-a409-7434d2780350},\n created = {2015-04-11T19:52:16.000Z},\n accessed = {2015-03-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The soft tick Ornithodoros erraticus occurs on pig farms in southern Portugal and Spain and transmits several important pathogens of humans and livestock. Its distribution is patchy and the determinants of its distribution are uncertain. Here, we use a Bayesian network model to explore possible associations between climate, farm management and the presence of O. erraticus. The resulting network confirms previous suggestions that the presence of O. erraticus is more likely in traditionally constructed pig housing, and indicates that carbon dioxide traps are highly effective for the detection of O. erraticus. Our approach also picked up several other intuitively reasonable relationships, including that traditional housing was more likely to be in poor condition and more likely to be out of use, and that buildings which were in use to house pigs were also less likely to be derelict. Neither temperature nor precipitation had any direct effect on the probability of the presence of O. erraticus, suggesting that the distribution of the species is primarily driven by farm management factors.},\n bibtype = {article},\n author = {Wilson, Anthony J and Ribeiro, Rita and Boinas, Fernando},\n doi = {10.1016/j.prevetmed.2013.02.006},\n journal = {Preventive veterinary medicine},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Using Bayesian networks to explore the role of weather as a potential determinant of disease in pigs.\n \n \n \n \n\n\n \n McCormick, B., J., J.; Sanchez-Vazquez, M., J.; and Lewis, F., I.\n\n\n \n\n\n\n Preventive veterinary medicine, 110(1): 54-63. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Using Bayesian networks to explore the role of weather as a potential determinant of disease in pigs.},\n type = {article},\n year = {2013},\n keywords = {Animals,Bayes Theorem,Climate,Great Britain,Models, Statistical,Regression Analysis,Risk Factors,Seasons,Swine,Swine Diseases,Swine Diseases: epidemiology,Swine Diseases: etiology,Weather},\n pages = {54-63},\n volume = {110},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587713000329},\n month = {5},\n day = {15},\n id = {cceab3aa-a4e2-34ed-b4cf-f71209685b8e},\n created = {2015-04-11T19:52:16.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Many pathogens are sensitive to climatic variables and this is reflected in their seasonality of occurrence and transmission. The identification of environmental conditions that influence disease occurrence can be subtle, particularly considering their complex interdependencies in addition to those relationships between climate and disease. Statistical treatment of environmental variables is often dependent on their correlations and thus descriptions of climate are often restricted to means rather than accounting for the more precise aspects (including mean, maximum, minimum, variability). Here we utilize a novel multivariate statistical modelling approach, additive Bayesian network (ABN) analyses, to identify the inter-linkages of different weather variables to better capture short-term environmental conditions that are important drivers of disease. We present a case study that explores weather as a driver of disease in livestock systems. We utilize quality assurance health scheme data on ten major diseases of pigs from 875 finishing pig herds distributed across the United Kingdom over 7 years (2005-2011). We examine the relationship between the occurrence of these pathologies and contemporary weather conditions measured by local meteorological stations. All ten pathologies were associated with at least 2 other pathologies (maximum 6). Three pathologies were associated directly with temperature variables: papular dermatitis, enzootic pneumonia and milk spots. Latitude was strongly associated with multiple pathologies, though associations with longitude were eliminated when clustering for repeated observations of farms was assessed. The identification of relationships between climatic factors and different (potentially related) diseases offers a more comprehensive insight into the complex role of seasonal drivers and herd health status than traditional analytical methods.},\n bibtype = {article},\n author = {McCormick, B J J and Sanchez-Vazquez, M J and Lewis, F I},\n doi = {10.1016/j.prevetmed.2013.02.001},\n journal = {Preventive veterinary medicine},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Identifying associations in Escherichia coli antimicrobial resistance patterns using additive Bayesian networks.\n \n \n \n \n\n\n \n Ludwig, A.; Berthiaume, P.; Boerlin, P.; Gow, S.; Léger, D.; and Lewis, F., I.\n\n\n \n\n\n\n Preventive veterinary medicine, 110(1): 64-75. 5 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Identifying$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Identifying associations in Escherichia coli antimicrobial resistance patterns using additive Bayesian networks.},\n type = {article},\n year = {2013},\n keywords = {Animals,Anti-Bacterial Agents,Anti-Bacterial Agents: pharmacology,Bayes Theorem,Canada,Drug Resistance, Multiple, Bacterial,Escherichia coli,Escherichia coli Infections,Escherichia coli Infections: epidemiology,Escherichia coli Infections: microbiology,Escherichia coli Infections: veterinary,Escherichia coli: drug effects,Feces,Feces: microbiology,Multivariate Analysis,Risk Factors,Seasons,Swine,Swine Diseases,Swine Diseases: epidemiology,Swine Diseases: microbiology},\n pages = {64-75},\n volume = {110},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587713000366},\n month = {5},\n day = {15},\n id = {20390ed2-fc37-3869-b04a-af71a1eff1a1},\n created = {2015-04-11T19:52:16.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {While the genesis of antimicrobial resistance (AMR) in animal production is a high profile topic in the media and the scientific community, it is still not well understood. The epidemiology of AMR is complex. This complexity is demonstrated by extensive biological and evolutionary mechanisms which are potentially impacted by farm management and husbandry practices - the risk factors. Many parts of this system have yet to be fully described. Notably, the occurrence of multiple resistance patterns is the rule rather than exception - the multivariate problem. A first essential step in the development of any comprehensive risk factor analysis - whose goal is the prevention or reduction of AMR - is to describe those associations between different patterns of resistance which are systematic. That is, have sufficient statistical support for these patterns to be considered robust features of the underlying epidemiological system, and whose presence must therefore be incorporated into any risk factor analysis of AMR for it to be meaningful with respect to the farm environment. Presented here is a case study that seeks to identify systematic associations between patterns of resistance to 13 different antimicrobials in Escherichia coli isolates obtained from composite finisher (>80 kg) pig faecal samples obtained from Canada's five major pork producing provinces. The use of a Bayesian network analysis approach allowed us to identify many systematic associations between individual antimicrobial resistances. Sixteen of these resistances are corroborated with existing literature. These associations are distributed between several important classes of antimicrobials including the β-lactams, folate biosynthesis inhibitors, tetracyclines, aminoglycosides and quinolones. This study presents an exciting first step towards the larger and far more ambitious goal of developing generic and holistic risk factor analyses for on-farm occurrence of AMR. Analyses of this nature would combine multivariate response variables (joint patterns of resistance) with multi-factorial causal factors from within the livestock production environment thereby permitting a more complete understanding of the epidemiology of antimicrobial resistance.},\n bibtype = {article},\n author = {Ludwig, Antoinette and Berthiaume, Philippe and Boerlin, Patrick and Gow, Sheryl and Léger, David and Lewis, Fraser I},\n doi = {10.1016/j.prevetmed.2013.02.005},\n journal = {Preventive veterinary medicine},\n number = {1}\n}

\n

\n\n\n

\n While the genesis of antimicrobial resistance (AMR) in animal production is a high profile topic in the media and the scientific community, it is still not well understood. The epidemiology of AMR is complex. This complexity is demonstrated by extensive biological and evolutionary mechanisms which are potentially impacted by farm management and husbandry practices - the risk factors. Many parts of this system have yet to be fully described. Notably, the occurrence of multiple resistance patterns is the rule rather than exception - the multivariate problem. A first essential step in the development of any comprehensive risk factor analysis - whose goal is the prevention or reduction of AMR - is to describe those associations between different patterns of resistance which are systematic. That is, have sufficient statistical support for these patterns to be considered robust features of the underlying epidemiological system, and whose presence must therefore be incorporated into any risk factor analysis of AMR for it to be meaningful with respect to the farm environment. Presented here is a case study that seeks to identify systematic associations between patterns of resistance to 13 different antimicrobials in Escherichia coli isolates obtained from composite finisher (>80 kg) pig faecal samples obtained from Canada's five major pork producing provinces. The use of a Bayesian network analysis approach allowed us to identify many systematic associations between individual antimicrobial resistances. Sixteen of these resistances are corroborated with existing literature. These associations are distributed between several important classes of antimicrobials including the β-lactams, folate biosynthesis inhibitors, tetracyclines, aminoglycosides and quinolones. This study presents an exciting first step towards the larger and far more ambitious goal of developing generic and holistic risk factor analyses for on-farm occurrence of AMR. Analyses of this nature would combine multivariate response variables (joint patterns of resistance) with multi-factorial causal factors from within the livestock production environment thereby permitting a more complete understanding of the epidemiology of antimicrobial resistance.\n

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\n \n 2012\n \n \n (3)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Handbook of Statistics Volume 28.\n \n \n \n \n\n\n \n Rodin, A., S.; Gogoshin, G.; Litvinenko, A.; and Boerwinkle, E.\n\n\n \n\n\n\n Volume 28 of Handbook of StatisticsElsevier, 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Handbook$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@book{\n title = {Handbook of Statistics Volume 28},\n type = {book},\n year = {2012},\n source = {Handbook of Statistics},\n keywords = {Bayesian (Belief) networks,data mining,genetic epidemiology,genomics,large-scale (genome-wide) association studies},\n pages = {479-510},\n volume = {28},\n websites = {http://www.sciencedirect.com/science/article/pii/B978044451875000018X},\n publisher = {Elsevier},\n series = {Handbook of Statistics},\n id = {af7a0e19-eb2b-34c5-8a80-c86d0ed17794},\n created = {2015-04-11T17:56:17.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Recent advances in DNA sequencing and genotyping technologies led to the initiation of large-scale genetic association studies aimed at unraveling complex genotype–(environment)–phenotype relationships underlying common human diseases. Unfortunately, traditional statistical tools are ill-suited for analyzing high dimensional datasets with many small effects. In addition, a primary emphasis of traditional statistical methods is formal hypothesis testing rather than hypothesis generation. When faced with hundreds of thousands (and soon—millions) of potentially predictive variables (e.g., SNPs), we need methods for automated knowledge discovery. Providentially, such methods have long been in development and are well-established in the data mining research community. One of such methods is Bayesian or Belief Network (BN) modeling, which has its roots in both computer science and statistics. A BN is a graphical model that represents a joint multivariate probability distribution and reflects the conditional independences among variables. Given data, the optimal network topology can be estimated with the assistance of local search (optimization) algorithms and model scoring criteria. Statistical significance of edge strengths can be evaluated using model scoring criteria tests, cross-validation and bootstrapping. BNs are an excellent tool for reverse-engineering biological (e.g., physiologic and genetic) networks from “flat” datasets (e.g., generated by the large-scale, or candidate gene, association studies, or microarray gene expression experiments). In this chapter we review various applications of BN methodology to modern human genomics. An example application is detailed. We also discuss various technical aspects of BN reconstruction, with a special emphasis on scalability in the context of modern “omic” data.},\n bibtype = {book},\n author = {Rodin, Andrei S. and Gogoshin, Grigoriy and Litvinenko, Anatoliy and Boerwinkle, Eric},\n doi = {10.1016/B978-0-44-451875-0.00018-X}\n}

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\n\n\n

\n \n\n \n \n \n \n \n \n A Bayesian network approach for selecting translocation sites for endangered island birds.\n \n \n \n \n\n\n \n Laws, R., J.; and Kesler, D., C.\n\n\n \n\n\n\n Biological Conservation, 155: 178-185. 10 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A Bayesian network approach for selecting translocation sites for endangered island birds},\n type = {article},\n year = {2012},\n keywords = {Assisted colonization,Bayesian network models,Island bird conservation,Island conservation,Micronesian kingfishers,Site selection,Translocation},\n pages = {178-185},\n volume = {155},\n websites = {http://www.sciencedirect.com/science/article/pii/S0006320712002650},\n month = {10},\n id = {6c212439-9dfe-3566-a9e9-370e188b471f},\n created = {2015-04-11T19:52:11.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Translocation has become increasingly important for conserving island species. Limited tools are available for guiding the selection of translocation sites, however, particularly when establishing rescue populations outside of historic ranges. We developed a Bayesian network model framework for translocation site selection for island birds. The model consisted of four primary components including ecological requirements for survival, anthropogenic threats at the population establishment site, effects the translocated species may have on native species, and operational support associated with the translocation process and ongoing management. We then used the model to identify potential sites for the establishment of a wild population of Guam Micronesian kingfishers (Todiramphus cinnamominus cinnamominus) on an island outside the bird’s historic range. Conditional probabilities that guided model evaluations were allocated using information from the literature, expert opinions, and a training set of islands outside the region under consideration for releases. The model was used to evaluate 239 islands where a translocation population of Micronesian kingfishers could be established. Five islands, all in the Federated States of Micronesia, were identified as being suitable for assisted colonization, including Kosrae, Yap, Faichuk, Weno and Fefan. Sensitivity analysis showed a correspondence between model variables and island characteristics indicated by the literature as being the most important for successful translocation. We found the Bayesian network model to be a useful tool for translocation site selection despite limited information on the natural history of the Guam Micronesian kingfisher and the factors that impact the success of translocations.},\n bibtype = {article},\n author = {Laws, Rebecca J. and Kesler, Dylan C.},\n doi = {10.1016/j.biocon.2012.05.016},\n journal = {Biological Conservation}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).\n \n \n \n \n\n\n \n Borchani, H.; Bielza, C.; Martı Nez-Martı N, P.; and Larrañaga, P.\n\n\n \n\n\n\n Journal of biomedical informatics, 45(6): 1175-84. 12 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Markov$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).},\n type = {article},\n year = {2012},\n keywords = {Bayes Theorem,Health Status,Humans,Markov Chains,Parkinson Disease,Quality of Life,Questionnaires},\n pages = {1175-84},\n volume = {45},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046412001074},\n month = {12},\n id = {afbab3b0-866d-31cd-a4de-a170b773e5f7},\n created = {2015-04-11T19:52:21.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.},\n bibtype = {article},\n author = {Borchani, Hanen and Bielza, Concha and Martı Nez-Martı N, Pablo and Larrañaga, Pedro},\n doi = {10.1016/j.jbi.2012.07.010},\n journal = {Journal of biomedical informatics},\n number = {6}\n}

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\n

\n \n 2011\n \n \n (3)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n A data mining approach to predictive vegetation mapping using probabilistic graphical models.\n \n \n \n \n\n\n \n Dlamini, W., M.\n\n\n \n\n\n\n Ecological Informatics, 6(2): 111-124. 3 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {A data mining approach to predictive vegetation mapping using probabilistic graphical models},\n type = {article},\n year = {2011},\n keywords = {Bayesian network,Data mining,Expectation-maximization,Graphical model,Predictive vegetation mapping},\n pages = {111-124},\n volume = {6},\n websites = {http://www.sciencedirect.com/science/article/pii/S1574954111000045},\n month = {3},\n id = {89b79dd3-8a40-3cc2-806e-ec8d21b65cdd},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-02-16},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper develops a novel method to model and predict the spatial distribution of vegetation types in Swaziland using physiographic and bioclimatic variables. The method uses a data mining approach implemented within a probabilistic graphical model to match two observed hierarchical levels of vegetation. The classification uses Bayesian networks (BN) and the parameterization is based on the expectation-maximization (EM) algorithm. The model is tested on a random sample of mapped vegetation types in Swaziland and allowed for the identification of the key environmental variables that are most important for capturing the vegetation spatial distribution. We show that while elevation and geology are the most important variables explaining the spatial distribution patterns of vegetation for both models, the influence of the climatic and other variables on the vegetation at the two levels differ. The overall distribution of the predicted vegetation classes was very similar to their distribution on the observed vegetation map. Overall the error rate was found to be 9.35% for a model of 16 vegetation classes and 4.9% for the one with 5 classes, indicating the excellent classification accuracy of the approach despite the complex landscape of the study area. Possible sources of error and some limitations are discussed and conclusions are drawn including suggestions for further investigation.},\n bibtype = {article},\n author = {Dlamini, Wisdom M.},\n doi = {10.1016/j.ecoinf.2010.12.005},\n journal = {Ecological Informatics},\n number = {2}\n}

\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data.\n \n \n \n \n\n\n \n Lewis, F.; Brülisauer, F.; and Gunn, G.\n\n\n \n\n\n\n Preventive Veterinary Medicine, 100(2): 109-115. 6 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Structure$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Structure discovery in Bayesian networks: An analytical tool for analysing complex animal health data},\n type = {article},\n year = {2011},\n keywords = {Knowledge Discovery},\n pages = {109-115},\n volume = {100},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587711000341},\n month = {6},\n id = {b2a35511-7e61-3ccf-8469-499cf4428d39},\n created = {2015-04-11T19:52:15.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Analysing animal health data can be a complex task as the health status of individuals or groups of animals, might depend on many inter-related variables. The objective is to differentiate variables that are directly associated with health status and therefore promising targets for intervention, from variables that are indirectly associated with health status and can therefore at best only affect this indirectly through association with other variables. Bayesian network (BN) modelling is a machine learning technique for empirically identifying associations in complex and high dimensional data, so-called “structure discovery”. An introduction to structure discovery using BN modelling is presented, comprising the key assumptions required by the methodology, along with a discussion of advantages and limitations. To demonstrate the various steps required to apply BN structure discovery to animal health data, illustrative analyses of data collected during a previously published study concerned with exposure to bovine viral diarrhoea virus in beef cow-calf herds in Scotland are presented.},\n bibtype = {article},\n author = {Lewis, F.I. and Brülisauer, F. and Gunn, G.J.},\n doi = {10.1016/j.prevetmed.2011.02.003},\n journal = {Preventive Veterinary Medicine},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Discovery of high-level tasks in the operating room.\n \n \n \n \n\n\n \n Bouarfa, L.; Jonker, P., P.; and Dankelman, J.\n\n\n \n\n\n\n Journal of biomedical informatics, 44(3): 455-62. 6 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Discovery$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Discovery of high-level tasks in the operating room.},\n type = {article},\n year = {2011},\n keywords = {Algorithms,Bayes Theorem,Computer-Assisted Instruction,Computer-Assisted Instruction: methods,General Surgery,General Surgery: education,Humans,Operating Rooms,Pilot Projects,Surgical Procedures, Operative,Task Performance and Analysis,User-Computer Interface,Video Recording},\n pages = {455-62},\n volume = {44},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046410000055},\n month = {6},\n id = {c791ad67-56df-3789-85ec-1557c47c6d82},\n created = {2015-04-11T19:52:21.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Recognizing and understanding surgical high-level tasks from sensor readings is important for surgical workflow analysis. Surgical high-level task recognition is also a challenging task in ubiquitous computing because of the inherent uncertainty of sensor data and the complexity of the operating room environment. In this paper, we present a framework for recognizing high-level tasks from low-level noisy sensor data. Specifically, we present a Markov-based approach for inferring high-level tasks from a set of low-level sensor data. We also propose to clean the noisy sensor data using a Bayesian approach. Preliminary results on a noise-free dataset of ten surgical procedures show that it is possible to recognize surgical high-level tasks with detection accuracies up to 90%. Introducing missed and ghost errors to the sensor data results in a significant decrease of the recognition accuracy. This supports our claim to use a cleaning algorithm before the training step. Finally, we highlight exciting research directions in this area.},\n bibtype = {article},\n author = {Bouarfa, L and Jonker, P P and Dankelman, J},\n doi = {10.1016/j.jbi.2010.01.004},\n journal = {Journal of biomedical informatics},\n number = {3}\n}

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\n\n\n\n\n\n

\n

\n \n 2010\n \n \n (3)\n \n \n

\n

\n \n \n

\n \n\n \n \n \n \n \n \n Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.\n \n \n \n \n\n\n \n Smid, J., H.; Verloo, D.; Barker, G., C.; and Havelaar, A., H.\n\n\n \n\n\n\n International journal of food microbiology, 139 Suppl : S57-63. 5 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Strengths$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment.},\n type = {article},\n year = {2010},\n keywords = {Bayes Theorem,Consumer Product Safety,Food Microbiology,Models, Theoretical,Monte Carlo Method,Risk Assessment},\n pages = {S57-63},\n volume = {139 Suppl },\n websites = {http://www.sciencedirect.com/science/article/pii/S0168160509006680},\n month = {5},\n day = {30},\n id = {119696cd-5483-3ada-9cef-d8bdfa313774},\n created = {2015-04-11T18:33:35.000Z},\n accessed = {2015-04-01},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We discuss different aspects of farm-to-fork risk assessment from a modelling perspective. Stochastic simulation models as they are presented today represent a mathematical representation of nature. In food safety risk assessment, a common modelling approach consists of a logic chain beginning at the source of the hazard and ending with the unwanted consequences of interest. This 'farm-to-fork' approach usually begins with the hazard on the farm, sometimes with different compartments presenting different parts of the production chain, and ends with the 'dose' received by the consumer or in case a dose response model is available the number of cases of illness. These models are typically implemented as Monte Carlo simulations, which are unidirectional in nature, and the link between statistics and simulation model is not interactive. A possible solution could be the use of Bayesian belief networks (BBNs) and this paper tries to discuss in an intuitive way the possibilities of using BBNs as an alternative for Monte Carlo modelling. An inventory is made of the strengths and weaknesses of both approaches, and an example is given showing an additional use of BBNs in biotracing problems.},\n bibtype = {article},\n author = {Smid, J H and Verloo, D and Barker, G C and Havelaar, A H},\n doi = {10.1016/j.ijfoodmicro.2009.12.015},\n journal = {International journal of food microbiology}\n}

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\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks.\n \n \n \n \n\n\n \n Kininmonth, S.; van Oppen, M., J.; and Possingham, H., P.\n\n\n \n\n\n\n Ecological Modelling, 221(24): 2870-2880. 12 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Determining$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks},\n type = {article},\n year = {2010},\n keywords = {Bayesian Belief Networks,Genetic communities,Genetic connectivity,Hydrodynamics,Microsatellites,Seriatopora hystrix},\n pages = {2870-2880},\n volume = {221},\n websites = {http://www.sciencedirect.com/science/article/pii/S030438001000459X},\n month = {12},\n id = {0a782806-f909-35da-a16c-3ab0be3307d9},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The exchange of genetic information between coral reefs through the transport of larvae can be described in terms of networks that capture the linkages between distant populations. A key question arising from these networks is the determination of the highly connected modules (communities). Communities can be defined using genetic similarity or distance statistics between multiple samples but due to limited specimen sampling capacity the boundaries of the communities for the known coral reefs in the seascape remain unresolved. In this study we use the microsatellite composition of individual corals to compare sample populations using a genetic dissimilarity measure (FST) which is then used to create a complex network. This network involved sampling 1025 colonies from 22 collection sites and examining 10 microsatellites loci. The links between each sampling site were given a strength that was created from the pair wise FST values. The result is an undirected weighted network describing the genetic dissimilarity between each sampled population. From this network we then determined the community structure using a leading eigenvector algorithm within graph theory. However, given the relatively limited sampling conducted, the representation of the regional genetic structure was incomplete. To assist with defining the boundaries of the genetically based communities we also integrated the communities derived from a hydrodynamic and distance based networks. The hydrodynamic network, though more comprehensive, was of smaller spatial extent than our genetic sampling. A Bayesian Belief network was developed to integrate the overlapping communities. The results indicate the genetic population structure of the Great Barrier Reef and provide guidance on where future genetic sampling should take place to complete the genetic diversity mapping.},\n bibtype = {article},\n author = {Kininmonth, Stuart and van Oppen, Madeleine J.H. and Possingham, Hugh P.},\n doi = {10.1016/j.ecolmodel.2010.08.042},\n journal = {Ecological Modelling},\n number = {24}\n}

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\n \n\n \n \n \n \n \n \n Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle.\n \n \n \n \n\n\n \n Johnson, S.; Mengersen, K.; de Waal, A.; Marnewick, K.; Cilliers, D.; Houser, A., M.; and Boast, L.\n\n\n \n\n\n\n Ecological Modelling, 221(4): 641-651. 2 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle},\n type = {article},\n year = {2010},\n keywords = {Acinonyx jubatus,Bayesian network,Cheetah metapopulation,IBNDC,Iterative approach,Predator human conflict,Relocation},\n pages = {641-651},\n volume = {221},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380009007947},\n month = {2},\n id = {89d659bf-886a-39bd-bfa1-35233230c196},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Relocation is one of the strategies used by conservationists to deal with problem cheetahs in southern Africa. The success of a relocation event and the factors that influence it within the broader context of long-term viability of wild cheetah metapopulations was the focus of a Bayesian Network (BN) modelling workshop in South Africa. Using a new heuristics, Iterative Bayesian Network Development Cycle (IBNDC), described in this paper, several networks were formulated to distinguish between the unique relocation experiences and conditions in Botswana and South Africa. There were many common underlying factors, despite the disparate relocation strategies and sites in the two countries. The benefit of relocation BNs goes beyond the identification and quantification of the factors influencing the success of relocations and population viability. They equip conservationists with a powerful communication tool in their negotiations with land and livestock owners, which is key to the long-term survival of cheetahs in southern Africa. Importantly, the IBNDC provides the ecological modeller with a methodological process that combines several BN design frameworks to facilitate the development of a BN in a multi-expert and multi-field domain.},\n bibtype = {article},\n author = {Johnson, Sandra and Mengersen, Kerrie and de Waal, Alta and Marnewick, Kelly and Cilliers, Deon and Houser, Ann Marie and Boast, Lorraine},\n doi = {10.1016/j.ecolmodel.2009.11.012},\n journal = {Ecological Modelling},\n number = {4}\n}

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\n \n 2009\n \n \n (5)\n \n \n

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\n \n\n \n \n \n \n \n \n Recovery or decline of the northwestern Black Sea: A societal choice revealed by socio-ecological modelling.\n \n \n \n \n\n\n \n Langmead, O.; McQuatters-Gollop, A.; Mee, L., D.; Friedrich, J.; Gilbert, A., J.; Gomoiu, M.; Jackson, E., L.; Knudsen, S.; Minicheva, G.; and Todorova, V.\n\n\n \n\n\n\n Ecological Modelling, 220(21): 2927-2939. 11 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Recovery$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Recovery or decline of the northwestern Black Sea: A societal choice revealed by socio-ecological modelling},\n type = {article},\n year = {2009},\n keywords = {Bayesian belief network (BBN),Black Sea,DPSIR,Eutrophication,Marine socio-ecological systems},\n pages = {2927-2939},\n volume = {220},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380008004420},\n month = {11},\n id = {4b653492-32d3-3a09-b599-3d3289914a4e},\n created = {2015-04-11T19:07:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {During recent decades anthropogenic activities have dramatically impacted the Black Sea ecosystem. High levels of riverine nutrient input during the 1970s and 1980s caused eutrophic conditions including intense algal blooms resulting in hypoxia and the subsequent collapse of benthic habitats on the northwestern shelf. Intense fishing pressure also depleted stocks of many apex predators, contributing to an increase in planktivorous fish that are now the focus of fishing efforts. Additionally, the Black Sea's ecosystem changed even further with the introduction of exotic species. Economic collapse of the surrounding socialist republics in the early 1990s resulted in decreased nutrient loading which has allowed the Black Sea ecosystem to start to recover, but under rapidly changing economic and political conditions, future recovery is uncertain. In this study we use a multidisciplinary approach to integrate information from socio-economic and ecological systems to model the effects of future development scenarios on the marine environment of the northwestern Black Sea shelf. The Driver–Pressure–State-Impact-Response framework was used to construct conceptual models, explicitly mapping impacts of socio-economic Drivers on the marine ecosystem. Bayesian belief networks (BBNs), a stochastic modelling technique, were used to quantify these causal relationships, operationalise models and assess the effects of alternative development paths on the Black Sea ecosystem. BBNs use probabilistic dependencies as a common metric, allowing the integration of quantitative and qualitative information. Under the Baseline Scenario, recovery of the Black Sea appears tenuous as the exploitation of environmental resources (agriculture, fishing and shipping) increases with continued economic development of post-Soviet countries. This results in the loss of wetlands through drainage and reclamation. Water transparency decreases as phytoplankton bloom and this deterioration in water quality leads to the degradation of coastal plant communities (Cystoseira, seagrass) and also Phyllophora habitat on the shelf. Decomposition of benthic plants results in hypoxia killing flora and fauna associated with these habitats. Ecological pressure from these factors along with constant levels of fishing activity results in target stocks remaining depleted. Of the four Alternative Scenarios, two show improvements on the Baseline ecosystem condition, with improved waste water treatment and reduced fishing pressure, while the other two show a worsening, due to increased natural resource exploitation leading to rapid reversal of any recent ecosystem recovery. From this we conclude that variations in economic policy have significant consequences for the health of the Black Sea, and ecosystem recovery is directly linked to social–economic choices.},\n bibtype = {article},\n author = {Langmead, Olivia and McQuatters-Gollop, Abigail and Mee, Laurence D. and Friedrich, Jana and Gilbert, Alison J. and Gomoiu, Marian-Traian and Jackson, Emma L. and Knudsen, Ståle and Minicheva, Galina and Todorova, Valentina},\n doi = {10.1016/j.ecolmodel.2008.09.011},\n journal = {Ecological Modelling},\n number = {21}\n}

\n

\n\n\n

\n During recent decades anthropogenic activities have dramatically impacted the Black Sea ecosystem. High levels of riverine nutrient input during the 1970s and 1980s caused eutrophic conditions including intense algal blooms resulting in hypoxia and the subsequent collapse of benthic habitats on the northwestern shelf. Intense fishing pressure also depleted stocks of many apex predators, contributing to an increase in planktivorous fish that are now the focus of fishing efforts. Additionally, the Black Sea's ecosystem changed even further with the introduction of exotic species. Economic collapse of the surrounding socialist republics in the early 1990s resulted in decreased nutrient loading which has allowed the Black Sea ecosystem to start to recover, but under rapidly changing economic and political conditions, future recovery is uncertain. In this study we use a multidisciplinary approach to integrate information from socio-economic and ecological systems to model the effects of future development scenarios on the marine environment of the northwestern Black Sea shelf. The Driver–Pressure–State-Impact-Response framework was used to construct conceptual models, explicitly mapping impacts of socio-economic Drivers on the marine ecosystem. Bayesian belief networks (BBNs), a stochastic modelling technique, were used to quantify these causal relationships, operationalise models and assess the effects of alternative development paths on the Black Sea ecosystem. BBNs use probabilistic dependencies as a common metric, allowing the integration of quantitative and qualitative information. Under the Baseline Scenario, recovery of the Black Sea appears tenuous as the exploitation of environmental resources (agriculture, fishing and shipping) increases with continued economic development of post-Soviet countries. This results in the loss of wetlands through drainage and reclamation. Water transparency decreases as phytoplankton bloom and this deterioration in water quality leads to the degradation of coastal plant communities (Cystoseira, seagrass) and also Phyllophora habitat on the shelf. Decomposition of benthic plants results in hypoxia killing flora and fauna associated with these habitats. Ecological pressure from these factors along with constant levels of fishing activity results in target stocks remaining depleted. Of the four Alternative Scenarios, two show improvements on the Baseline ecosystem condition, with improved waste water treatment and reduced fishing pressure, while the other two show a worsening, due to increased natural resource exploitation leading to rapid reversal of any recent ecosystem recovery. From this we conclude that variations in economic policy have significant consequences for the health of the Black Sea, and ecosystem recovery is directly linked to social–economic choices.\n

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\n \n\n \n \n \n \n \n \n Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.\n \n \n \n \n\n\n \n Ettema, J., F.; Østergaard, S.; and Kristensen, A., R.\n\n\n \n\n\n\n Preventive veterinary medicine, 92(1-2): 89-98. 11 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Estimation$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.},\n type = {article},\n year = {2009},\n keywords = {Animal Husbandry,Animals,Cattle,Cattle Diseases,Cattle Diseases: epidemiology,Cattle Diseases: prevention & control,Cross-Sectional Studies,Denmark,Denmark: epidemiology,Dermatitis,Dermatitis: epidemiology,Dermatitis: prevention & control,Dermatitis: veterinary,Female,Foot Diseases,Foot Diseases: epidemiology,Foot Diseases: prevention & control,Foot Diseases: veterinary,Hoof and Claw,Lactation,Markov Chains,Monte Carlo Method,Risk Factors,Stochastic Processes},\n pages = {89-98},\n volume = {92},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587709002323},\n month = {11},\n day = {1},\n id = {a10fa027-a792-31bb-a581-c9f96003cece},\n created = {2015-04-11T19:52:16.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration. Besides, the data has been used to estimate the random effect of herd on disease prevalence and to find conditional probabilities of cows being lame, given the presence of the three diseases. By considering the 50 herds representative for the Danish population, the estimates for risk factors, conditional probabilities and random herd effects are used to formulate cow-level probability distributions of disease presence in a specific Danish dairy herd. By step-wise inclusion of information on cow- and herd-level risk factors, lameness prevalence and clinical diagnosis of diseases on cows in the herd, the Bayesian network systematically adjusts the probability distributions for disease presence in the specific herd. Information on population-, herd- and cow-level is combined and the uncertainty in inference on disease probability is quantified.},\n bibtype = {article},\n author = {Ettema, Jehan Frans and Østergaard, Søren and Kristensen, Anders Ringgaard},\n doi = {10.1016/j.prevetmed.2009.08.014},\n journal = {Preventive veterinary medicine},\n number = {1-2}\n}

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\n \n\n \n \n \n \n \n \n An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds.\n \n \n \n \n\n\n \n Jensen, T., B.; Kristensen, A., R.; Toft, N.; Baadsgaard, N., P.; Østergaard, S.; and Houe, H.\n\n\n \n\n\n\n Preventive Veterinary Medicine, 89(3-4): 237-248. 6 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"An$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds},\n type = {article},\n year = {2009},\n keywords = {Bayesian networks,Diagnostic test procedures,Finisher herds,Leg disorders,Risk factors},\n pages = {237-248},\n volume = {89},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587709000476},\n month = {6},\n id = {fac4f9b1-6ad8-3ebc-88ad-5d149140175f},\n created = {2015-04-11T19:52:16.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The implementation of an effective control strategy against disease in a finisher herd requires knowledge regarding the disease level in the herd. A Bayesian network was constructed that can estimate risk indexes for three cause-categories of leg disorders in a finisher herd. The cause-categories of leg disorders were divided into infectious causes (arthritis caused by infectious pathogens), physical causes (e.g. fracture and claw lesions), and inherited causes (osteochondrosis). Information about the herd (e.g. the herd size, floor type and number of suppliers) and information about individual pigs (e.g. results from diagnostic tests) were used to estimate the most likely cause of leg disorders at herd level. As information to the model originated from two different levels, we used an object-oriented structure in order to ease the specification of the Bayesian network. Hence, a Herd class and a Pig class comprised the basic components of the object-oriented structure. The causal structure of the model was based on evidence from published literature. The conditional probabilities used in the model were elicited from experts within the field and from the published literature. To illustrate the behaviour of the model, we investigated the value of different levels of evidence in two fictitious herds with different herd characteristics related to the risk of leg disorders (e.g. purchase policy, production type and the stocking density in pens). The model enabled us to demonstrate the value of performing systematic collection of additional information (i.e. clinical, pathological and bacteriological examination) when identifying causes of leg disorders at herd level.},\n bibtype = {article},\n author = {Jensen, Tina Birk and Kristensen, Anders Ringgaard and Toft, Nils and Baadsgaard, Niels Peter and Østergaard, Søren and Houe, Hans},\n doi = {10.1016/j.prevetmed.2009.02.009},\n journal = {Preventive Veterinary Medicine},\n number = {3-4}\n}

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\n \n\n \n \n \n \n \n \n Similarity-Based Virtual Screening with a Bayesian Inference Network.\n \n \n \n \n\n\n \n Abdo, A.; and Salim, N.\n\n\n \n\n\n\n ChemMedChem, 4(2): 210-218. 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Similarity-Based$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Similarity-Based Virtual Screening with a Bayesian Inference Network},\n type = {article},\n year = {2009},\n keywords = {Bayesian networks,drug discovery,inference networks,similarity searching,virtual screening},\n pages = {210-218},\n volume = {4},\n websites = {http://dx.doi.org/10.1002/cmdc.200800290},\n publisher = {WILEY-VCH Verlag},\n id = {1d351968-88d8-3519-8eba-7424d3f67b67},\n created = {2015-04-12T17:48:21.000Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {CMDC:CMDC200800290},\n source_type = {article},\n private_publication = {false},\n abstract = {An inference network model for molecular similarity searching: The similarity search problem is modeled using inference or evidential reasoning under uncertainty. The inference network model treats similarity searching as an evidential reasoning process in which multiple sources of evidence about compounds and reference structures are combined to estimate resemblance probabilities.Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.},\n bibtype = {article},\n author = {Abdo, Ammar and Salim, Naomie},\n doi = {10.1002/cmdc.200800290},\n journal = {ChemMedChem},\n number = {2}\n}

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\n \n\n \n \n \n \n \n \n Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach.\n \n \n \n \n\n\n \n Renken, H.; and Mumby, P., J.\n\n\n \n\n\n\n Ecological Modelling, 220(9-10): 1305-1314. 5 2009.\n \n\n\n\n
\n\n\n\n \n \n $\"Modelling$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach},\n type = {article},\n year = {2009},\n keywords = {Bayesian belief network,Diadema antillarum,Dictyota spp.,Grazing pressure,Macroalgal dynamics,Nutrients,Scaridae},\n pages = {1305-1314},\n volume = {220},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380009001525},\n month = {5},\n id = {030ea028-6a17-3a9d-aaf6-b71ba8694f93},\n created = {2015-04-12T18:12:42.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.},\n bibtype = {article},\n author = {Renken, Henk and Mumby, Peter J.},\n doi = {10.1016/j.ecolmodel.2009.02.022},\n journal = {Ecological Modelling},\n number = {9-10}\n}

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\n \n 2008\n \n \n (5)\n \n \n

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\n \n\n \n \n \n \n \n \n Monitoring amphibian populations with incomplete survey information using a Bayesian probabilistic model.\n \n \n \n \n\n\n \n Wilson, D., S.; Stoddard, M., A.; and Puettmann, K., J.\n\n\n \n\n\n\n Ecological Modelling, 214(2-4): 210-218. 6 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Monitoring$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Monitoring amphibian populations with incomplete survey information using a Bayesian probabilistic model},\n type = {article},\n year = {2008},\n keywords = {Amphibian monitoring,Ascaphus truei,Bayesian networks,Dicamptodon tenebrosus,Hierarchical Bayesian models,Rhyacotriton spp.},\n pages = {210-218},\n volume = {214},\n websites = {http://www.sciencedirect.com/science/article/pii/S0304380008000768},\n month = {6},\n id = {338a2458-588d-355a-ad68-6ca16fa7786d},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Bayesian networks (BNs) are a probabilistic modeling platform that connect variables through a series of conditional dependences. We demonstrate their utility for broad-scale conservation of amphibian populations where different types of information may be available within the region. Wildlife conservation decisions for most species are made jointly with other objectives and are tightly constrained by finances. Bayesian networks allow the use of all available information in predictions, and can provide managers with the best available information for making decisions. Habitat models were developed as a hierarchical Bayesian (HB) model for aquatic amphibian populations in the temperate Oregon Coast Range, USA. Predictions for new streams sections were made jointly using a Bayesian network to allow the inclusion of different types of available information. Missing habitat variables were modeled based on habitat survey information. Uncertainty in the true (but unknown) habitat variables were incorporated into the prediction intervals. Further, the probabilistic approach allowed us to incorporate survey information for co-occurring species to help make better predictions. Such species information was connected through the Bayesian network by the conditional dependence that arises from shared habitat variables. The utility of Bayesian networks was shown for these populations for broad-scale risk management. In contrast to deterministic models, the probabilistic nature of Bayesian networks is a natural platform for incorporating uncertainty in predictions and inference.},\n bibtype = {article},\n author = {Wilson, Duncan S. and Stoddard, Margo A. and Puettmann, Klaus J.},\n doi = {10.1016/j.ecolmodel.2008.02.003},\n journal = {Ecological Modelling},\n number = {2-4}\n}

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\n\n\n

\n \n\n \n \n \n \n \n \n Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin.\n \n \n \n \n\n\n \n Barton, D.; Saloranta, T.; Moe, S.; Eggestad, H.; and Kuikka, S.\n\n\n \n\n\n\n Ecological Economics, 66(1): 91-104. 5 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Bayesian belief networks as a meta-modelling tool in integrated river basin management — Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin},\n type = {article},\n year = {2008},\n keywords = {Bayesian networks,Benefit–cost analysis,Decision analysis,Eutrophication,Influence diagrams,Uncertainty,Water Framework Directive},\n pages = {91-104},\n volume = {66},\n websites = {http://www.sciencedirect.com/science/article/pii/S0921800908000827},\n month = {5},\n id = {766417a3-f775-3807-9b17-11a887b43ad7},\n created = {2015-04-11T19:51:57.000Z},\n accessed = {2015-02-19},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A Bayesian network approach is used to conduct decision analysis of nutrient abatement measures in the Morsa catchment, South Eastern Norway. The paper demonstrates the use of Bayesian networks as a meta-modelling tool in integrated river basin management (IRBM) for structuring and combining the probabilistic information available in existing cost-effectiveness studies, eutrophication models and data, non-market valuation studies and expert opinion. The Bayesian belief network is used to evaluate eutrophication mitigation costs relative to benefits, as part of the economic analysis under the EU Water Framework Directive (WFD). Pros and cons of Bayesian networks as reported in the literature are reviewed in light of the results from our Morsa catchment model. The reported advantages of Bayesian networks in promoting integrated, inter-disciplinary evaluation of uncertainty in IRBM, as well as the apparent advantages for risk communication with stakeholders, are offset in our case by the cost of obtaining reliable probabilistic data and meta-model validation procedures.},\n bibtype = {article},\n author = {Barton, D.N. and Saloranta, T. and Moe, S.J. and Eggestad, H.O. and Kuikka, S.},\n doi = {10.1016/j.ecolecon.2008.02.012},\n journal = {Ecological Economics},\n number = {1}\n}

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\n \n\n \n \n \n \n \n \n Evolution and challenges in the design of computational systems for triage assistance.\n \n \n \n \n\n\n \n Abad-Grau, M., M.; Ierache, J.; Cervino, C.; and Sebastiani, P.\n\n\n \n\n\n\n Journal of biomedical informatics, 41(3): 432-41. 6 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Evolution$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Evolution and challenges in the design of computational systems for triage assistance.},\n type = {article},\n year = {2008},\n keywords = {Artificial Intelligence,Decision Support Systems, Clinical,Decision Support Systems, Clinical: organization &,Software,Software Design,Triage,Triage: methods,Triage: organization & administration},\n pages = {432-41},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046408000130},\n month = {6},\n id = {0543131c-4370-3941-8695-4e67e936c805},\n created = {2015-04-11T19:52:21.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Compared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems.},\n bibtype = {article},\n author = {Abad-Grau, María M and Ierache, Jorge and Cervino, Claudio and Sebastiani, Paola},\n doi = {10.1016/j.jbi.2008.01.007},\n journal = {Journal of biomedical informatics},\n number = {3}\n}

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\n \n\n \n \n \n \n \n \n Encyclopedia of Ecology.\n \n \n \n \n\n\n \n Borsuk, M.\n\n\n \n\n\n\n Elsevier, 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Encyclopedia$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@book{\n title = {Encyclopedia of Ecology},\n type = {book},\n year = {2008},\n source = {Encyclopedia of Ecology},\n keywords = {ANOVA,Artificial intelligence,Artificial neural networks,Bayesian networks,Bayesian statistics,Belief networks,Calibration,Causal analysis,Causality,Classification and regression tree,Correlated data,Generalized additive model,Generalized linear model,Graphical models,Hierarchical Bayes,Hierarchical models,Inference,Influence diagrams,Learning,Linear regression,Nonlinear regression,Prediction,Probability networks,Quantile regression,Structural equations},\n pages = {307-317},\n websites = {http://www.sciencedirect.com/science/article/pii/B9780080454054001440,http://www.sciencedirect.com/science/article/pii/B9780080454054002342},\n publisher = {Elsevier},\n id = {bede31d1-9b18-3c5a-97a2-a518a22c9ea0},\n created = {2015-04-12T18:12:42.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Statistical models help ecologists use the patterns they find in their data to make predictions. Such predictions are needed to explore the implications of alternative hypotheses, anticipate the outcomes of possible experimental designs, and provide decision support to ecosystem managers. A wide range of model frameworks are appropriate for prediction depending on the nature of the data. This article starts with the most restricted and familiar setting – linear regression analysis – and proceeds by considering increasingly general, regression-based techniques which sequentially drop some limiting assumptions. These include generalized linear models, quantile regression, nonlinear regression, and generalized additive models. Some special cases of regression, such as correlated data models, structural equation models, classification and regression trees, and artificial neural networks, are also described. Nonregression-based methods are presented next, including hierarchical models, Bayesian models, and belief networks. These methods are generally better able to deal with situations of multiple plausible models or overparametrized model formulations. The article concludes by discussing issues of model-based prediction, including multimodel averaging.},\n bibtype = {book},\n author = {Borsuk, M.E.},\n doi = {10.1016/B978-008045405-4.00144-0}\n}

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\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Using Bayesian networks to examine consistent trends in fish farm benthic impact studies.\n \n \n \n \n\n\n \n Giles, H.\n\n\n \n\n\n\n Aquaculture, 274(2-4): 181-195. 2 2008.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Using Bayesian networks to examine consistent trends in fish farm benthic impact studies},\n type = {article},\n year = {2008},\n keywords = {Aquaculture,Bayesian network,Fish farming,Footprint,Monitoring,Review},\n pages = {181-195},\n volume = {274},\n websites = {http://www.sciencedirect.com/science/article/pii/S0044848607010915},\n month = {2},\n id = {48f537e3-0d8d-3f40-b15e-9bea8a1c0811},\n created = {2015-04-12T18:12:42.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Fish farming in coastal marine environments results in the deposition of waste products on the seafloor and can cause changes in sediment chemistry and community structure. Rapid growth in the industry and an increased awareness and sensitivity to the environmental impacts over the last decade have resulted in more stringent compliance requirements for aquaculture producers. However, the selection of monitoring parameters is surrounded by uncertainty, which impedes the development of cost-effective monitoring procedures. Studies examining benthic impacts of fish farms have come to different conclusions concerning the severity of impacts. Previous attempts to quantitatively review these studies have had only limited success due to the uncertainty in the data originating from discrepancies in sampling and analytical protocols as well as different temporal and spatial scales covered in the studies. The main objective of this study was to review publications on fish farm benthic impacts and to develop a Bayesian network (BN) for the quantitative assessment of the relationships between impact parameters and site and farm characteristics. A BN was constructed based on parameters relationships obtained from 64 studies. It showed that benthic impact was a function of fish density, farm volume, food conversion ratio, water depth, current strength and sediment mud content. To examine the sensitivity of benthic impact variables to changes in these parameters, the BN was used to calculate three scenarios representing low, moderate and high impact. Porewater sulphide, acid volatile sulphide (AVS-S), water content, redox potential, sediment oxygen consumption, sediment NH4 release and macrofauna diversity showed the most certain and sensitive responses in these scenarios but methodological limitations have to be taken into consideration before characterising them as reliable monitoring parameters for a specific application. An examination of spatial trends in benthic impact parameters suggested that fish farm impacts were confined to a radius of about 40 to 70 m around the farms studied. The inability to satisfactorily model parameters as a function of distance from farms demonstrated the complexity of their spatial distribution and highlighted the need to improve our understanding of farm footprints to avoid detrimental environmental effects as a consequence of culture intensification. The BN approach successfully identified reliable monitoring parameters based on reviewed impact studies and has potential to support cost-effective monitoring. For use at specific farms, site-specific data should be integrated into the BN and additional variables could easily be added. BNs enable the synthesis of scientific and industry research data, practical experience and stakeholders’ perspectives, which are important in the development of monitoring guidelines that meet the needs of all parties.},\n bibtype = {article},\n author = {Giles, H.},\n doi = {10.1016/j.aquaculture.2007.11.020},\n journal = {Aquaculture},\n number = {2-4}\n}

\n

\n\n\n

\n Fish farming in coastal marine environments results in the deposition of waste products on the seafloor and can cause changes in sediment chemistry and community structure. Rapid growth in the industry and an increased awareness and sensitivity to the environmental impacts over the last decade have resulted in more stringent compliance requirements for aquaculture producers. However, the selection of monitoring parameters is surrounded by uncertainty, which impedes the development of cost-effective monitoring procedures. Studies examining benthic impacts of fish farms have come to different conclusions concerning the severity of impacts. Previous attempts to quantitatively review these studies have had only limited success due to the uncertainty in the data originating from discrepancies in sampling and analytical protocols as well as different temporal and spatial scales covered in the studies. The main objective of this study was to review publications on fish farm benthic impacts and to develop a Bayesian network (BN) for the quantitative assessment of the relationships between impact parameters and site and farm characteristics. A BN was constructed based on parameters relationships obtained from 64 studies. It showed that benthic impact was a function of fish density, farm volume, food conversion ratio, water depth, current strength and sediment mud content. To examine the sensitivity of benthic impact variables to changes in these parameters, the BN was used to calculate three scenarios representing low, moderate and high impact. Porewater sulphide, acid volatile sulphide (AVS-S), water content, redox potential, sediment oxygen consumption, sediment NH4 release and macrofauna diversity showed the most certain and sensitive responses in these scenarios but methodological limitations have to be taken into consideration before characterising them as reliable monitoring parameters for a specific application. An examination of spatial trends in benthic impact parameters suggested that fish farm impacts were confined to a radius of about 40 to 70 m around the farms studied. The inability to satisfactorily model parameters as a function of distance from farms demonstrated the complexity of their spatial distribution and highlighted the need to improve our understanding of farm footprints to avoid detrimental environmental effects as a consequence of culture intensification. The BN approach successfully identified reliable monitoring parameters based on reviewed impact studies and has potential to support cost-effective monitoring. For use at specific farms, site-specific data should be integrated into the BN and additional variables could easily be added. BNs enable the synthesis of scientific and industry research data, practical experience and stakeholders’ perspectives, which are important in the development of monitoring guidelines that meet the needs of all parties.\n

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\n \n 2007\n \n \n (2)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Diagnosis of breast cancer using Bayesian networks: a case study.\n \n \n \n \n\n\n \n Cruz-Ramírez, N.; Acosta-Mesa, H., G.; Carrillo-Calvet, H.; Nava-Fernández, L., A.; and Barrientos-Martínez, R., E.\n\n\n \n\n\n\n Computers in biology and medicine, 37(11): 1553-64. 11 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Diagnosis$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Diagnosis of breast cancer using Bayesian networks: a case study.},\n type = {article},\n year = {2007},\n keywords = {Algorithms,Bayes Theorem,Biopsy, Fine-Needle,Breast Neoplasms,Breast Neoplasms: diagnosis,Cytodiagnosis,Cytodiagnosis: statistics & numerical data,Databases, Factual,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: statistics & numeric,Female,Humans,Observer Variation},\n pages = {1553-64},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000340},\n month = {11},\n id = {c377f910-0ac0-3e74-99dd-fe19572d3efa},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.},\n bibtype = {article},\n author = {Cruz-Ramírez, Nicandro and Acosta-Mesa, Héctor Gabriel and Carrillo-Calvet, Humberto and Nava-Fernández, Luis Alonso and Barrientos-Martínez, Rocío Erandi},\n doi = {10.1016/j.compbiomed.2007.02.003},\n journal = {Computers in biology and medicine},\n number = {11}\n}

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\n \n\n \n \n \n \n \n \n Learning and modeling biosignatures from tissue images.\n \n \n \n \n\n\n \n Gilfeather, F.; Hamine, V.; Helman, P.; Hutt, J.; Loring, T.; Lyons, C., R.; and Veroff, R.\n\n\n \n\n\n\n Computers in biology and medicine, 37(11): 1539-52. 11 2007.\n \n\n\n\n
\n\n\n\n \n \n $\"Learning$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

\n

@article{\n title = {Learning and modeling biosignatures from tissue images.},\n type = {article},\n year = {2007},\n keywords = {Algorithms,Animals,Artificial Intelligence,Bayes Theorem,Computer Simulation,Diagnosis, Computer-Assisted,Humans,Image Interpretation, Computer-Assisted,Infection,Infection: classification,Infection: diagnosis,Lung,Lung Diseases,Lung Diseases: diagnosis,Lung: anatomy & histology,Mice},\n pages = {1539-52},\n volume = {37},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482507000339},\n month = {11},\n id = {67ef01ad-da43-3128-83b2-5641240dded9},\n created = {2015-04-11T19:51:59.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and nai ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.},\n bibtype = {article},\n author = {Gilfeather, Frank and Hamine, Vikas and Helman, Paul and Hutt, Julie and Loring, Terry and Lyons, C Rick and Veroff, Robert},\n doi = {10.1016/j.compbiomed.2007.02.005},\n journal = {Computers in biology and medicine},\n number = {11}\n}

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\n \n 2005\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Probabilistic representation of the exposure of consumers to Clostridium botulinum neurotoxin in a minimally processed potato product.\n \n \n \n \n\n\n \n Barker, G., C.; Malakar, P., K.; Del Torre, M.; Stecchini, M., L.; and Peck, M., W.\n\n\n \n\n\n\n International journal of food microbiology, 100(1-3): 345-57. 4 2005.\n \n\n\n\n
\n\n\n\n \n \n $\"Probabilistic$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Probabilistic representation of the exposure of consumers to Clostridium botulinum neurotoxin in a minimally processed potato product.},\n type = {article},\n year = {2005},\n keywords = {Bayes Theorem,Botulinum Toxins,Botulinum Toxins: administration & dosage,Botulinum Toxins: biosynthesis,Clostridium botulinum,Clostridium botulinum: growth & development,Clostridium botulinum: isolation & purification,Clostridium botulinum: metabolism,Consumer Product Safety,Food Contamination,Food Contamination: analysis,Food Handling,Food Handling: methods,Food Microbiology,Food Packaging,Food Packaging: methods,Food Preservation,Food Preservation: methods,Models, Biological,Solanum tuberosum,Solanum tuberosum: microbiology,Time Factors},\n pages = {345-57},\n volume = {100},\n websites = {http://www.sciencedirect.com/science/article/pii/S0168160504004921},\n month = {4},\n day = {15},\n id = {76b67d39-6fb9-33f3-9458-76ac3ccf5d69},\n created = {2015-04-11T17:55:11.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We have examined the potential of a well-specified, minimally processed potato product as a vehicle for the exposure of consumers to Clostridium botulinum neurotoxin. The product is a relatively simple combination of raw potato flakes, flour, starch and other minor ingredients and has an extended lifetime under refrigeration conditions. A combination of information and data, from a variety of sources that includes the manufacturer, has shown that the product is particularly safe with respect to non-proteolytic C. botulinum hazards. The model concentrates on a simple end point, the toxicity of an individual retail unit of the product at the point of consumer preparation, which is related to an individual risk. The probabilistic analysis was built using Bayesian Belief Network (BBN) techniques.},\n bibtype = {article},\n author = {Barker, G C and Malakar, P K and Del Torre, M and Stecchini, M L and Peck, M W},\n doi = {10.1016/j.ijfoodmicro.2004.10.030},\n journal = {International journal of food microbiology},\n number = {1-3}\n}

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\n \n 2004\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Classification of fluorescence in situ hybridization images using belief networks.\n \n \n \n \n\n\n \n Malka, R.; and Lerner, B.\n\n\n \n\n\n\n Pattern Recognition Letters, 25(16): 1777-1785. 12 2004.\n \n\n\n\n
\n\n\n\n \n \n $\"Classification$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Classification of fluorescence in situ hybridization images using belief networks},\n type = {article},\n year = {2004},\n keywords = {Belief networks,Fluorescence in situ hybridization (FISH),Image classification,K2 algorithm,Naive Bayesian classifier},\n pages = {1777-1785},\n volume = {25},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167865504001710},\n month = {12},\n id = {68ade94f-ed7b-3d73-9cf9-97890a76a62c},\n created = {2015-04-11T18:33:34.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning.},\n bibtype = {article},\n author = {Malka, Roy and Lerner, Boaz},\n doi = {10.1016/j.patrec.2004.07.010},\n journal = {Pattern Recognition Letters},\n number = {16}\n}

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\n \n 2003\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.\n \n \n \n \n\n\n \n Lee, S.; and Abbott, P., A.\n\n\n \n\n\n\n Journal of Biomedical Informatics, 36(4-5): 389-399. 8 2003.\n \n\n\n\n
\n\n\n\n \n \n $\"Bayesian$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers},\n type = {article},\n year = {2003},\n keywords = {Bayesian network,Data mining,Knowledge discovery,Nursing research},\n pages = {389-399},\n volume = {36},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046403001035},\n month = {8},\n id = {f47c27da-5ef4-3cb6-bc1a-6a03fd92aaef},\n created = {2015-04-11T19:52:22.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.},\n bibtype = {article},\n author = {Lee, Sun-Mi and Abbott, Patricia A.},\n doi = {10.1016/j.jbi.2003.09.022},\n journal = {Journal of Biomedical Informatics},\n number = {4-5}\n}

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\n \n 2002\n \n \n (1)\n \n \n

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\n \n\n \n \n \n \n \n \n A meta-assessment for elasmobranchs based on dietary data and Bayesian networks.\n \n \n \n \n\n\n \n Hammond, T.; and Ellis, J.\n\n\n \n\n\n\n Ecological Indicators, 1(3): 197-211. 3 2002.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {A meta-assessment for elasmobranchs based on dietary data and Bayesian networks},\n type = {article},\n year = {2002},\n keywords = {Bayesian networks,Elasmobranchs,Fisheries management,Food webs,Threatened species},\n pages = {197-211},\n volume = {1},\n websites = {http://www.sciencedirect.com/science/article/pii/S1470160X02000055},\n month = {3},\n id = {5ff64a37-b644-3122-b19b-a0a56f36cbb9},\n created = {2015-04-11T19:51:56.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We developed a new approach, meta-assessment, as a tool for identifying declining (and potentially threatened) fish stocks in situations where a lack of data precludes traditional stock assessments. Meta-assessments are models enhanced by the incorporation of other stock assessment results. We used this idea to estimate historic biomass trends for demersal elasmobranchs of the Irish Sea. Bayesian networks, constructed from published dietary data and resembling food webs, allowed us to incorporate into our estimates the results from virtual population analysis (VPA) for Irish Sea cod, sole, plaice and whiting. To assess accuracy, we used cross-validation, estimating historic biomass trends in each individual VPA species from trends in the other three plus trends in fishing effort. We compared predicted annual trends to those derived from VPA and found 66% accuracy. We also compared biomass trends estimated from annual trawl surveys to corresponding network predictions, recovering survey trends correctly 61% of the time for elasmobranchs, 78% of the time for gurnards (Triglidae) and 89% for bib and pout (Trisopterus spp.). Results suggest that of the 11 elasmobranchs examined, the angel shark (Squatina squatina) increased in biomass least frequently from 1987 to 1997, a view consistent with survey results. Our approach also suggested a marked decline in common skate (Dipturus batis) over the period 1965–1978, during which time the skate was extirpated from the Irish Sea. We conclude that meta-assessment can serve as an exploratory method for identifying potentially threatened stocks, where even landings data are unavailable.},\n bibtype = {article},\n author = {Hammond, T.R and Ellis, J.R},\n doi = {10.1016/S1470-160X(02)00005-5},\n journal = {Ecological Indicators},\n number = {3}\n}

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\n \n 2001\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement.\n \n \n \n \n\n\n \n Marcot, B., G.; Holthausen, R., S.; Raphael, M., G.; Rowland, M., M.; and Wisdom, M., J.\n\n\n \n\n\n\n Forest Ecology and Management, 153(1-3): 29-42. 10 2001.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement},\n type = {article},\n year = {2001},\n keywords = {Bayesian belief networks,Bayesian statistics,Columbia River,Fish modeling,Interior Columbia basin,Population viability,Wildlife modeling},\n pages = {29-42},\n volume = {153},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378112701004522},\n month = {10},\n id = {cc2c9060-a8ac-3419-affc-7fb712a87df9},\n created = {2015-04-11T18:46:33.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {We developed procedures for using Bayesian belief networks (BBNs) to model habitat and population viability of selected at-risk fish and wildlife species. The BBN models represent the ecological causal web of key environmental correlates (KECs) that most influence habitat capability, potential population response for each species, and influence of habitat planning alternatives. BBN models represent site-specific KECs, habitat capability at the subwatershed level, and pattern of habitat capability across all subwatersheds. BBNs use Dirichlet prior probability distributions and standard Bayesian updating of posterior probabilities. We derived estimates of prior and conditional probabilities from a mix of empirical data and expert judgment, mostly the latter. Sensitivity analyses identified planning decisions and KECs that most influence species outcomes, and can help prioritize monitoring activities. BBN models, however, substitute for neither field studies nor empirical, quantitative population viability analyses of population demography and genetics.},\n bibtype = {article},\n author = {Marcot, Bruce G and Holthausen, Richard S and Raphael, Martin G and Rowland, Mary M and Wisdom, Michael J},\n doi = {10.1016/S0378-1127(01)00452-2},\n journal = {Forest Ecology and Management},\n number = {1-3}\n}

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\n \n 2000\n \n \n (1)\n \n \n

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\n \n \n

\n \n\n \n \n \n \n \n \n Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases.\n \n \n \n \n\n\n \n McKendrick, I.; Gettinby, G.; Gu, Y.; Reid, S.; and Revie, C.\n\n\n \n\n\n\n Preventive Veterinary Medicine, 47(3): 141-156. 11 2000.\n \n\n\n\n
\n\n\n\n \n \n $\"Using$ Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{\n title = {Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases},\n type = {article},\n year = {2000},\n keywords = {Africa,Bayesian belief network,Cattle disease,Differential diagnosis,Expert system},\n pages = {141-156},\n volume = {47},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167587700001720},\n month = {11},\n id = {6c2d4651-008c-38ba-9985-0715a04f671f},\n created = {2015-04-11T18:46:32.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2017-03-14T14:28:38.949Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The examination of presenting signs has always played an important role in the diagnosis of diseases in animal populations. In the case of diseases of tropical cattle, such expertise is often scarce and confined to those experts with many years of experience. To capture, conserve and disseminate such valuable expert knowledge remains a key challenge to the application of knowledge-based systems in veterinary medicine. In this communication, we explore the use of a Bayesian belief network to quantify expert opinion with a view to estimating the likelihood of various diseases in the presence and absence of certain signs. Information was elicited from a panel of 44 experienced veterinarians to provide the response matrix of 27 signs associated with 20 commonly occurring diseases in sub-Saharan cattle. Using this prior information, estimates of the probability of certain signs occurring with each disease were calculated from which the Bayesian belief network was able to propagate the posterior probability of each of the diseases based on the observed signs. The method as an aid in making diagnosis is discussed. It is recognised that such an approach is but one strand in the process of arriving at a diagnosis. For ease of use and accessibility, the approach has been converted into the software program CaDDiS (Cattle Disease Diagnosis System) which is available for consultation on the World Wide Web.},\n bibtype = {article},\n author = {McKendrick, I.J and Gettinby, G and Gu, Y and Reid, S.W.J and Revie, C.W},\n doi = {10.1016/S0167-5877(00)00172-0},\n journal = {Preventive Veterinary Medicine},\n number = {3}\n}

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