var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers\",\"type\":\"article\",\"year\":\"2003\",\"keywords\":\"Bayesian network,Data mining,Knowledge discovery,Nursing research\",\"pages\":\"389-399\",\"volume\":\"36\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1532046403001035\",\"month\":\"8\",\"id\":\"2a30872d-864b-36ba-9a57-f71218d1cd44\",\"created\":\"2015-04-12T19:14:39.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2017-03-14T14:39:16.063Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"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.\",\"bibtype\":\"article\",\"author\":\"Lee, Sun-Mi and Abbott, Patricia A.\",\"doi\":\"10.1016/j.jbi.2003.09.022\",\"journal\":\"Journal of Biomedical Informatics\",\"number\":\"4-5\",\"bibtex\":\"@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 = {2a30872d-864b-36ba-9a57-f71218d1cd44},\\n created = {2015-04-12T19:14:39.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2017-03-14T14:39:16.063Z},\\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}\",\"author_short\":[\"Lee,  S.\",\"Abbott,  P., A.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1532046403001035\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Data mining\",\"Knowledge discovery\",\"Nursing research\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours.\",\"type\":\"article\",\"year\":\"2013\",\"keywords\":\"Algorithms,Artificial Intelligence,Automation,Bayes Theorem,Breast Neoplasms,Breast Neoplasms: genetics,Breast Neoplasms: metabolism,Computer Simulation,Female,Gene Expression Profiling,Gene Expression Regulation, Neoplastic,Humans,Lipid Metabolism,Models, Genetic,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Oncogenes,Ovarian Neoplasms,Ovarian Neoplasms: genetics,Prognosis,Trans-Activators,Trans-Activators: genetics,Trans-Activators: metabolism\",\"pages\":\"334-41\",\"volume\":\"43\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0010482512002065\",\"month\":\"5\",\"id\":\"d5a3b899-1115-37e0-accc-99ea302c6cf1\",\"created\":\"2015-04-12T20:17:32.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2017-03-14T14:39:16.063Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently amplified in breast and ovarian cancer, and correlated with shorter patient survival in these cancers. To properly address the difficulties in managing complex gene interactions given our limited sample, statistical significance of edge strengths was evaluated using bootstrapping and the less reliable edges were pruned to increase the network robustness. We found that 13 out of the 35 genes associated with deregulated ZNF217 expression in breast tumours have been previously associated with survival and/or prognosis in cancers. Identifying genes involved in lipid metabolism opens new fields of investigation to decipher the molecular mechanisms driven by the ZNF217 oncogene. Moreover, nine of the 13 genes have already been identified as putative ZNF217 targets by independent biological studies. We therefore suggest that the algorithms for inferring local BNs are valuable data mining tools for unraveling complex mechanisms of biological pathways from expression data. The source code is available at http://www710.univ-lyon1.fr/∼aaussem/Software.html.\",\"bibtype\":\"article\",\"author\":\"Prestat, Emmanuel and de Morais, Sérgio Rodrigues and Vendrell, Julie A and Thollet, Aurélie and Gautier, Christian and Cohen, Pascale A and Aussem, Alex\",\"doi\":\"10.1016/j.compbiomed.2012.12.002\",\"journal\":\"Computers in biology and medicine\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours.},\\n type = {article},\\n year = {2013},\\n keywords = {Algorithms,Artificial Intelligence,Automation,Bayes Theorem,Breast Neoplasms,Breast Neoplasms: genetics,Breast Neoplasms: metabolism,Computer Simulation,Female,Gene Expression Profiling,Gene Expression Regulation, Neoplastic,Humans,Lipid Metabolism,Models, Genetic,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Oncogenes,Ovarian Neoplasms,Ovarian Neoplasms: genetics,Prognosis,Trans-Activators,Trans-Activators: genetics,Trans-Activators: metabolism},\\n pages = {334-41},\\n volume = {43},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482512002065},\\n month = {5},\\n id = {d5a3b899-1115-37e0-accc-99ea302c6cf1},\\n created = {2015-04-12T20:17:32.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2017-03-14T14:39:16.063Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently amplified in breast and ovarian cancer, and correlated with shorter patient survival in these cancers. To properly address the difficulties in managing complex gene interactions given our limited sample, statistical significance of edge strengths was evaluated using bootstrapping and the less reliable edges were pruned to increase the network robustness. We found that 13 out of the 35 genes associated with deregulated ZNF217 expression in breast tumours have been previously associated with survival and/or prognosis in cancers. Identifying genes involved in lipid metabolism opens new fields of investigation to decipher the molecular mechanisms driven by the ZNF217 oncogene. Moreover, nine of the 13 genes have already been identified as putative ZNF217 targets by independent biological studies. We therefore suggest that the algorithms for inferring local BNs are valuable data mining tools for unraveling complex mechanisms of biological pathways from expression data. The source code is available at http://www710.univ-lyon1.fr/∼aaussem/Software.html.},\\n bibtype = {article},\\n author = {Prestat, Emmanuel and de Morais, Sérgio Rodrigues and Vendrell, Julie A and Thollet, Aurélie and Gautier, Christian and Cohen, Pascale A and Aussem, Alex},\\n doi = {10.1016/j.compbiomed.2012.12.002},\\n journal = {Computers in biology and medicine},\\n number = {4}\\n}\",\"author_short\":[\"Prestat,  E.\",\"de Morais,  S., R.\",\"Vendrell,  J., A.\",\"Thollet,  A.\",\"Gautier,  C.\",\"Cohen,  P., A.\",\"Aussem,  A.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0010482512002065\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013\",\"role\":\"author\",\"keyword\":[\"Algorithms\",\"Artificial Intelligence\",\"Automation\",\"Bayes Theorem\",\"Breast Neoplasms\",\"Breast Neoplasms: genetics\",\"Breast Neoplasms: metabolism\",\"Computer Simulation\",\"Female\",\"Gene Expression Profiling\",\"Gene Expression Regulation\",\"Neoplastic\",\"Humans\",\"Lipid Metabolism\",\"Models\",\"Genetic\",\"Oligonucleotide Array Sequence Analysis\",\"Oligonucleotide Array Sequence Analysis: methods\",\"Oncogenes\",\"Ovarian Neoplasms\",\"Ovarian Neoplasms: genetics\",\"Prognosis\",\"Trans-Activators\",\"Trans-Activators: genetics\",\"Trans-Activators: metabolism\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Effective connectivity analysis of fMRI and MEG data collected under identical paradigms\",\"type\":\"article\",\"year\":\"2011\",\"keywords\":\"Bayesian networks,Effective connectivity,MEG,fMRI\",\"pages\":\"1156-1165\",\"volume\":\"41\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0010482511000825\",\"month\":\"12\",\"id\":\"7d1289d2-7b8e-3052-836e-5cb4ddd3fb0a\",\"created\":\"2015-04-12T20:17:36.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2017-03-14T14:39:16.063Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.\",\"bibtype\":\"article\",\"author\":\"Plis, Sergey M. and Weisend, Michael P. and Damaraju, Eswar and Eichele, Tom and Mayer, Andy and Clark, Vincent P. and Lane, Terran and Calhoun, Vince D.\",\"doi\":\"10.1016/j.compbiomed.2011.04.011\",\"journal\":\"Computers in Biology and Medicine\",\"number\":\"12\",\"bibtex\":\"@article{\\n title = {Effective connectivity analysis of fMRI and MEG data collected under identical paradigms},\\n type = {article},\\n year = {2011},\\n keywords = {Bayesian networks,Effective connectivity,MEG,fMRI},\\n pages = {1156-1165},\\n volume = {41},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482511000825},\\n month = {12},\\n id = {7d1289d2-7b8e-3052-836e-5cb4ddd3fb0a},\\n created = {2015-04-12T20:17:36.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2017-03-14T14:39:16.063Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.},\\n bibtype = {article},\\n author = {Plis, Sergey M. and Weisend, Michael P. and Damaraju, Eswar and Eichele, Tom and Mayer, Andy and Clark, Vincent P. and Lane, Terran and Calhoun, Vince D.},\\n doi = {10.1016/j.compbiomed.2011.04.011},\\n journal = {Computers in Biology and Medicine},\\n number = {12}\\n}\",\"author_short\":[\"Plis,  S., M.\",\"Weisend,  M., P.\",\"Damaraju,  E.\",\"Eichele,  T.\",\"Mayer,  A.\",\"Clark,  V., P.\",\"Lane,  T.\",\"Calhoun,  V., D.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0010482511000825\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011\",\"role\":\"author\",\"keyword\":[\"Bayesian networks\",\"Effective connectivity\",\"MEG\",\"fMRI\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis\",\"type\":\"article\",\"websites\":\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\",\"id\":\"e35eda2f-e1b6-32f7-bb1c-75d3ef758b08\",\"created\":\"2018-03-31T21:53:37.985Z\",\"accessed\":\"2018-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2018-03-31T21:53:37.985Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.\",\"bibtype\":\"article\",\"author\":\"Venkatesh, Tejas\",\"bibtex\":\"@article{\\n title = {Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis},\\n type = {article},\\n websites = {http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf},\\n id = {e35eda2f-e1b6-32f7-bb1c-75d3ef758b08},\\n created = {2018-03-31T21:53:37.985Z},\\n accessed = {2018-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2018-03-31T21:53:37.985Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.},\\n bibtype = {article},\\n author = {Venkatesh, Tejas}\\n}\",\"author_short\":[\"Venkatesh,  T.\"],\"urls\":{\"Website\":\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Effect of a Synthetic Feline Pheromone for Managing Unwanted Scratching\",\"type\":\"article\",\"year\":\"2018\",\"volume\":\"16\",\"websites\":\"http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf\",\"id\":\"1fe94015-943a-3440-9216-7efe3025ba3e\",\"created\":\"2018-04-10T13:57:02.281Z\",\"accessed\":\"2018-04-07\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2018-04-10T13:57:02.281Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"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.\",\"bibtype\":\"article\",\"author\":\"Beck, A. and De Jaeger, X. and Collin, J.-F. and Tynes, V.\",\"journal\":\"Intern J Appl Res Vet Med\",\"number\":\"1\",\"bibtex\":\"@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 = {1fe94015-943a-3440-9216-7efe3025ba3e},\\n created = {2018-04-10T13:57:02.281Z},\\n accessed = {2018-04-07},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2018-04-10T13:57:02.281Z},\\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}\",\"author_short\":[\"Beck,  A.\",\"De Jaeger,  X.\",\"Collin,  J.\",\"Tynes,  V.\"],\"urls\":{\"Website\":\"http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"beck-dejaeger-collin-tynes-effectofasyntheticfelinepheromoneformanagingunwantedscratching-2018\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network\",\"type\":\"article\",\"year\":\"2018\",\"pages\":\"e0199768\",\"volume\":\"13\",\"websites\":\"http://dx.plos.org/10.1371/journal.pone.0199768\",\"month\":\"7\",\"publisher\":\"Public Library of Science\",\"day\":\"12\",\"id\":\"de6ebdfd-6ec6-34fd-a603-6b2363ec936b\",\"created\":\"2018-07-18T22:19:23.145Z\",\"accessed\":\"2018-07-17\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2018-07-18T22:19:23.145Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely how multiple chronic conditions emerge among patients. We propose an unsupervised multi-level temporal Bayesian network to provide a compact representation of the relationship among emergence of multiple chronic conditions and patient level risk factors over time. To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages, starting with learning the inter-relationship of comorbidities within each year, followed by learning the intra-relationship of comorbidity emergence between consecutive years, and finally learning the hierarchical relationship of comorbidities and patient level risk factors. We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. Using a de-identified dataset of more than 250,000 patients receiving care from the U.S. Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised and semi-supervised learning approaches, as well as multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering focusing on traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), and back pain (BaPa). Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. These findings also revealed that the unsupervised approach has improved performance over multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.\",\"bibtype\":\"article\",\"author\":\"Faruqui, Syed Hasib Akhter and Alaeddini, Adel and Jaramillo, Carlos A. and Potter, Jennifer S. and Pugh, Mary Jo\",\"editor\":\"Miller, Mark Webber\",\"doi\":\"10.1371/journal.pone.0199768\",\"journal\":\"PLOS ONE\",\"number\":\"7\",\"bibtex\":\"@article{\\n title = {Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network},\\n type = {article},\\n year = {2018},\\n pages = {e0199768},\\n volume = {13},\\n websites = {http://dx.plos.org/10.1371/journal.pone.0199768},\\n month = {7},\\n publisher = {Public Library of Science},\\n day = {12},\\n id = {de6ebdfd-6ec6-34fd-a603-6b2363ec936b},\\n created = {2018-07-18T22:19:23.145Z},\\n accessed = {2018-07-17},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2018-07-18T22:19:23.145Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely how multiple chronic conditions emerge among patients. We propose an unsupervised multi-level temporal Bayesian network to provide a compact representation of the relationship among emergence of multiple chronic conditions and patient level risk factors over time. To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages, starting with learning the inter-relationship of comorbidities within each year, followed by learning the intra-relationship of comorbidity emergence between consecutive years, and finally learning the hierarchical relationship of comorbidities and patient level risk factors. We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. Using a de-identified dataset of more than 250,000 patients receiving care from the U.S. Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised and semi-supervised learning approaches, as well as multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering focusing on traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), and back pain (BaPa). Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. These findings also revealed that the unsupervised approach has improved performance over multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.},\\n bibtype = {article},\\n author = {Faruqui, Syed Hasib Akhter and Alaeddini, Adel and Jaramillo, Carlos A. and Potter, Jennifer S. and Pugh, Mary Jo},\\n editor = {Miller, Mark Webber},\\n doi = {10.1371/journal.pone.0199768},\\n journal = {PLOS ONE},\\n number = {7}\\n}\",\"author_short\":[\"Faruqui,  S., H., A.\",\"Alaeddini,  A.\",\"Jaramillo,  C., A.\",\"Potter,  J., S.\",\"Pugh,  M., J.\"],\"editor_short\":[\"Miller,  M., 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networks\",\"type\":\"misc\",\"id\":\"0580e39e-0461-33e9-bd28-ea2af7142410\",\"created\":\"2020-03-13T20:31:06.558Z\",\"accessed\":\"2020-03-13\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2020-03-13T20:31:06.643Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"bibtype\":\"misc\",\"author\":\"Nicholson, Ann\",\"bibtex\":\"@misc{\\n title = {Epidemiological data mining of cardiovascular Bayesian networks},\\n type = {misc},\\n id = {0580e39e-0461-33e9-bd28-ea2af7142410},\\n created = {2020-03-13T20:31:06.558Z},\\n accessed = {2020-03-13},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2020-03-13T20:31:06.643Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n bibtype = {misc},\\n author = {Nicholson, Ann}\\n}\",\"author_short\":[\"Nicholson,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"nicholson-epidemiologicaldataminingofcardiovascularbayesiannetworks\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk\",\"type\":\"article\",\"year\":\"2016\",\"keywords\":\"Bayesian networks,Cardiovascular lost years,Cardiovascular risk score,Causal dependency discovery,Metabolic syndrome,Model averaging\",\"pages\":\"128-142\",\"volume\":\"126\",\"month\":\"4\",\"publisher\":\"Elsevier Ireland Ltd\",\"day\":\"1\",\"id\":\"bb967501-eb4d-3400-802d-de3c68f37f48\",\"created\":\"2020-03-17T22:32:56.551Z\",\"accessed\":\"2020-03-17\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"ad14e5fb-797f-35df-9a65-790d8a1f2cc8\",\"last_modified\":\"2020-03-17T22:32:56.657Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":false,\"hidden\":false,\"private_publication\":false,\"abstract\":\"An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool.\",\"bibtype\":\"article\",\"author\":\"Fuster-Parra, P. and Tauler, P. and Bennasar-Veny, M. and Ligeza, A. and López-González, A. A. and Aguiló, A.\",\"doi\":\"10.1016/j.cmpb.2015.12.010\",\"journal\":\"Computer Methods and Programs in Biomedicine\",\"bibtex\":\"@article{\\n title = {Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk},\\n type = {article},\\n year = {2016},\\n keywords = {Bayesian networks,Cardiovascular lost years,Cardiovascular risk score,Causal dependency discovery,Metabolic syndrome,Model averaging},\\n pages = {128-142},\\n volume = {126},\\n month = {4},\\n publisher = {Elsevier Ireland Ltd},\\n day = {1},\\n id = {bb967501-eb4d-3400-802d-de3c68f37f48},\\n created = {2020-03-17T22:32:56.551Z},\\n accessed = {2020-03-17},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\\n last_modified = {2020-03-17T22:32:56.657Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {false},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool.},\\n bibtype = {article},\\n author = {Fuster-Parra, P. and Tauler, P. and Bennasar-Veny, M. and Ligeza, A. and López-González, A. A. and Aguiló, A.},\\n doi = {10.1016/j.cmpb.2015.12.010},\\n journal = {Computer Methods and Programs in Biomedicine}\\n}\",\"author_short\":[\"Fuster-Parra,  P.\",\"Tauler,  P.\",\"Bennasar-Veny,  M.\",\"Ligeza,  A.\",\"López-González,  A., A.\",\"Aguiló,  A.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"fusterparra-tauler-bennasarveny-ligeza-lpezgonzlez-aguil-bayesiannetworkmodelingacasestudyofanepidemiologicsystemanalysisofcardiovascularrisk-2016\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Bayesian networks\",\"Cardiovascular lost years\",\"Cardiovascular risk score\",\"Causal dependency discovery\",\"Metabolic syndrome\",\"Model averaging\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@article{
 title = {Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers},
 type = {article},
 year = {2003},
 keywords = {Bayesian network,Data mining,Knowledge discovery,Nursing research},
 pages = {389-399},
 volume = {36},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046403001035},
 month = {8},
 id = {2a30872d-864b-36ba-9a57-f71218d1cd44},
 created = {2015-04-12T19:14:39.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2017-03-14T14:39:16.063Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Lee, Sun-Mi and Abbott, Patricia A.},
 doi = {10.1016/j.jbi.2003.09.022},
 journal = {Journal of Biomedical Informatics},
 number = {4-5}
}

@article{
 title = {Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours.},
 type = {article},
 year = {2013},
 keywords = {Algorithms,Artificial Intelligence,Automation,Bayes Theorem,Breast Neoplasms,Breast Neoplasms: genetics,Breast Neoplasms: metabolism,Computer Simulation,Female,Gene Expression Profiling,Gene Expression Regulation, Neoplastic,Humans,Lipid Metabolism,Models, Genetic,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Oncogenes,Ovarian Neoplasms,Ovarian Neoplasms: genetics,Prognosis,Trans-Activators,Trans-Activators: genetics,Trans-Activators: metabolism},
 pages = {334-41},
 volume = {43},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482512002065},
 month = {5},
 id = {d5a3b899-1115-37e0-accc-99ea302c6cf1},
 created = {2015-04-12T20:17:32.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2017-03-14T14:39:16.063Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently amplified in breast and ovarian cancer, and correlated with shorter patient survival in these cancers. To properly address the difficulties in managing complex gene interactions given our limited sample, statistical significance of edge strengths was evaluated using bootstrapping and the less reliable edges were pruned to increase the network robustness. We found that 13 out of the 35 genes associated with deregulated ZNF217 expression in breast tumours have been previously associated with survival and/or prognosis in cancers. Identifying genes involved in lipid metabolism opens new fields of investigation to decipher the molecular mechanisms driven by the ZNF217 oncogene. Moreover, nine of the 13 genes have already been identified as putative ZNF217 targets by independent biological studies. We therefore suggest that the algorithms for inferring local BNs are valuable data mining tools for unraveling complex mechanisms of biological pathways from expression data. The source code is available at http://www710.univ-lyon1.fr/∼aaussem/Software.html.},
 bibtype = {article},
 author = {Prestat, Emmanuel and de Morais, Sérgio Rodrigues and Vendrell, Julie A and Thollet, Aurélie and Gautier, Christian and Cohen, Pascale A and Aussem, Alex},
 doi = {10.1016/j.compbiomed.2012.12.002},
 journal = {Computers in biology and medicine},
 number = {4}
}

@article{
 title = {Effective connectivity analysis of fMRI and MEG data collected under identical paradigms},
 type = {article},
 year = {2011},
 keywords = {Bayesian networks,Effective connectivity,MEG,fMRI},
 pages = {1156-1165},
 volume = {41},
 websites = {http://www.sciencedirect.com/science/article/pii/S0010482511000825},
 month = {12},
 id = {7d1289d2-7b8e-3052-836e-5cb4ddd3fb0a},
 created = {2015-04-12T20:17:36.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2017-03-14T14:39:16.063Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.},
 bibtype = {article},
 author = {Plis, Sergey M. and Weisend, Michael P. and Damaraju, Eswar and Eichele, Tom and Mayer, Andy and Clark, Vincent P. and Lane, Terran and Calhoun, Vince D.},
 doi = {10.1016/j.compbiomed.2011.04.011},
 journal = {Computers in Biology and Medicine},
 number = {12}
}

@article{
 title = {Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis},
 type = {article},
 websites = {http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf},
 id = {e35eda2f-e1b6-32f7-bb1c-75d3ef758b08},
 created = {2018-03-31T21:53:37.985Z},
 accessed = {2018-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2018-03-31T21:53:37.985Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.},
 bibtype = {article},
 author = {Venkatesh, Tejas}
}

@article{
 title = {Effect of a Synthetic Feline Pheromone for Managing Unwanted Scratching},
 type = {article},
 year = {2018},
 volume = {16},
 websites = {http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf},
 id = {1fe94015-943a-3440-9216-7efe3025ba3e},
 created = {2018-04-10T13:57:02.281Z},
 accessed = {2018-04-07},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2018-04-10T13:57:02.281Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Beck, A. and De Jaeger, X. and Collin, J.-F. and Tynes, V.},
 journal = {Intern J Appl Res Vet Med},
 number = {1}
}

@article{
 title = {Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network},
 type = {article},
 year = {2018},
 pages = {e0199768},
 volume = {13},
 websites = {http://dx.plos.org/10.1371/journal.pone.0199768},
 month = {7},
 publisher = {Public Library of Science},
 day = {12},
 id = {de6ebdfd-6ec6-34fd-a603-6b2363ec936b},
 created = {2018-07-18T22:19:23.145Z},
 accessed = {2018-07-17},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2018-07-18T22:19:23.145Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely how multiple chronic conditions emerge among patients. We propose an unsupervised multi-level temporal Bayesian network to provide a compact representation of the relationship among emergence of multiple chronic conditions and patient level risk factors over time. To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages, starting with learning the inter-relationship of comorbidities within each year, followed by learning the intra-relationship of comorbidity emergence between consecutive years, and finally learning the hierarchical relationship of comorbidities and patient level risk factors. We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. Using a de-identified dataset of more than 250,000 patients receiving care from the U.S. Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised and semi-supervised learning approaches, as well as multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering focusing on traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), and back pain (BaPa). Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. These findings also revealed that the unsupervised approach has improved performance over multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.},
 bibtype = {article},
 author = {Faruqui, Syed Hasib Akhter and Alaeddini, Adel and Jaramillo, Carlos A. and Potter, Jennifer S. and Pugh, Mary Jo},
 editor = {Miller, Mark Webber},
 doi = {10.1371/journal.pone.0199768},
 journal = {PLOS ONE},
 number = {7}
}

@article{
 title = {Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks},
 type = {article},
 year = {2019},
 pages = {807-822},
 volume = {10},
 websites = {http://www.berjournal.com/?p=4515},
 month = {7},
 day = {29},
 id = {176f9203-b4b8-32a5-b916-846a5bee24f2},
 created = {2019-07-29T18:11:51.728Z},
 accessed = {2019-07-29},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2019-07-29T18:11:51.809Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Uyar, Umut and Hatipoglu, Fatma Busem},
 doi = {10.20409/berj.2019.202},
 journal = {Business and Economics Research Journal},
 number = {4}
}

@article{
 title = {Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model},
 type = {article},
 year = {2019},
 pages = {7694-7716},
 volume = {55},
 websites = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024901},
 month = {9},
 day = {9},
 id = {b821d4f5-8a23-39d1-8615-f7cd33704304},
 created = {2019-12-29T15:54:11.893Z},
 accessed = {2019-12-29},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2019-12-29T15:54:11.960Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Ravindranath, Arun and Devineni, Naresh and Lall, Upmanu and Cook, Edward R. and Pederson, Greg and Martin, Justin and Woodhouse, Connie},
 doi = {10.1029/2019WR024901},
 journal = {Water Resources Research},
 number = {9}
}

@article{
 title = {Probabilistic computational model for correlated wind speed, solar irradiation, and load using Bayesian network},
 type = {article},
 year = {2020},
 pages = {1-1},
 websites = {https://ieeexplore.ieee.org/document/9020145/},
 id = {c9659621-7e9e-3bc0-9bc7-9d2acedb9749},
 created = {2020-03-10T13:48:38.595Z},
 accessed = {2020-03-10},
 file_attached = {true},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2020-03-10T13:48:38.692Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Wang, Hongtao and Zou, Bin},
 doi = {10.1109/ACCESS.2020.2977727},
 journal = {IEEE Access}
}

@misc{
 title = {Epidemiological data mining of cardiovascular Bayesian networks},
 type = {misc},
 id = {0580e39e-0461-33e9-bd28-ea2af7142410},
 created = {2020-03-13T20:31:06.558Z},
 accessed = {2020-03-13},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2020-03-13T20:31:06.643Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {misc},
 author = {Nicholson, Ann}
}

@article{
 title = {Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk},
 type = {article},
 year = {2016},
 keywords = {Bayesian networks,Cardiovascular lost years,Cardiovascular risk score,Causal dependency discovery,Metabolic syndrome,Model averaging},
 pages = {128-142},
 volume = {126},
 month = {4},
 publisher = {Elsevier Ireland Ltd},
 day = {1},
 id = {bb967501-eb4d-3400-802d-de3c68f37f48},
 created = {2020-03-17T22:32:56.551Z},
 accessed = {2020-03-17},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},
 last_modified = {2020-03-17T22:32:56.657Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool.},
 bibtype = {article},
 author = {Fuster-Parra, P. and Tauler, P. and Bennasar-Veny, M. and Ligeza, A. and López-González, A. A. and Aguiló, A.},
 doi = {10.1016/j.cmpb.2015.12.010},
 journal = {Computer Methods and Programs in Biomedicine}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/ad14e5fb-797f-35df-9a65-790d8a1f2cc8?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/ad14e5fb-797f-35df-9a65-790d8a1f2cc8?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/ad14e5fb-797f-35df-9a65-790d8a1f2cc8?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/14a26397-d240-ea77-1650-3f6f94d2ce1b/full_text.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/14a26397-d240-ea77-1650-3f6f94d2ce1b/full_text.pdf.pdf', event);\">\n    Probabilistic computational model for correlated wind speed, solar irradiation, and load using Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  H.;  and Zou,  B.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Access</i>,1-1.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/14a26397-d240-ea77-1650-3f6f94d2ce1b/full_text.pdf.pdf\"\n     onclick=\"log_download('wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020', 'https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/file/14a26397-d240-ea77-1650-3f6f94d2ce1b/full_text.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Probabilistic computational model for correlated wind speed, solar irradiation, and load using Bayesian network [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9020145/\"\n     onclick=\"log_download('wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020', 'https://ieeexplore.ieee.org/document/9020145/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Probabilistic computational model for correlated wind speed, solar irradiation, and load using Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ACCESS.2020.2977727\"\n     onclick=\"log_download('wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020', 'http://doi.org/10.1109/ACCESS.2020.2977727', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-zou-probabilisticcomputationalmodelforcorrelatedwindspeedsolarirradiationandloadusingbayesiannetwork-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Probabilistic computational model for correlated wind speed, solar irradiation, and load using Bayesian network},\n type = {article},\n year = {2020},\n pages = {1-1},\n websites = {https://ieeexplore.ieee.org/document/9020145/},\n id = {c9659621-7e9e-3bc0-9bc7-9d2acedb9749},\n created = {2020-03-10T13:48:38.595Z},\n accessed = {2020-03-10},\n file_attached = {true},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2020-03-10T13:48:38.692Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Wang, Hongtao and Zou, Bin},\n doi = {10.1109/ACCESS.2020.2977727},\n journal = {IEEE Access}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"uyar-hatipoglu-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.berjournal.com/?p=4515\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'uyar-hatipoglu-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'http://www.berjournal.com/?p=4515', event);\">\n    Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Uyar,  U.;  and Hatipoglu,  F., B.\n</span>\n\n  \n\n</span>\n\n  <i>Business and Economics Research Journal</i>, 10(4): 807-822. 7 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.berjournal.com/?p=4515\"\n     onclick=\"log_download('uyar-hatipoglu-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'http://www.berjournal.com/?p=4515', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.20409/berj.2019.202\"\n     onclick=\"log_download('uyar-hatipoglu-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019', 'http://doi.org/10.20409/berj.2019.202', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/uyar-hatipoglu-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('uyar-hatipoglu-examiningthedynamicsofmacroeconomicindicatorsandbankingstockreturnswithbayesiannetworks-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Examining the Dynamics of Macroeconomic Indicators and Banking Stock Returns with Bayesian Networks},\n type = {article},\n year = {2019},\n pages = {807-822},\n volume = {10},\n websites = {http://www.berjournal.com/?p=4515},\n month = {7},\n day = {29},\n id = {176f9203-b4b8-32a5-b916-846a5bee24f2},\n created = {2019-07-29T18:11:51.728Z},\n accessed = {2019-07-29},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2019-07-29T18:11:51.809Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Uyar, Umut and Hatipoglu, Fatma Busem},\n doi = {10.20409/berj.2019.202},\n journal = {Business and Economics Research Journal},\n number = {4}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ravindranath-devineni-lall-cook-pederson-martin-woodhouse-streamflowreconstructionintheuppermissouririverbasinusinganovelbayesiannetworkmodel-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024901\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ravindranath-devineni-lall-cook-pederson-martin-woodhouse-streamflowreconstructionintheuppermissouririverbasinusinganovelbayesiannetworkmodel-2019', 'https://onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024901', event);\">\n    Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ravindranath,  A.; Devineni,  N.; Lall,  U.; Cook,  E., R.; Pederson,  G.; Martin,  J.;  and Woodhouse,  C.\n</span>\n\n  \n\n</span>\n\n  <i>Water Resources Research</i>, 55(9): 7694-7716. 9 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024901\"\n     onclick=\"log_download('ravindranath-devineni-lall-cook-pederson-martin-woodhouse-streamflowreconstructionintheuppermissouririverbasinusinganovelbayesiannetworkmodel-2019', 'https://onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024901', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1029/2019WR024901\"\n     onclick=\"log_download('ravindranath-devineni-lall-cook-pederson-martin-woodhouse-streamflowreconstructionintheuppermissouririverbasinusinganovelbayesiannetworkmodel-2019', 'http://doi.org/10.1029/2019WR024901', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ravindranath-devineni-lall-cook-pederson-martin-woodhouse-streamflowreconstructionintheuppermissouririverbasinusinganovelbayesiannetworkmodel-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ravindranath-devineni-lall-cook-pederson-martin-woodhouse-streamflowreconstructionintheuppermissouririverbasinusinganovelbayesiannetworkmodel-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Streamflow Reconstruction in the Upper Missouri River Basin Using a Novel Bayesian Network Model},\n type = {article},\n year = {2019},\n pages = {7694-7716},\n volume = {55},\n websites = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2019WR024901},\n month = {9},\n day = {9},\n id = {b821d4f5-8a23-39d1-8615-f7cd33704304},\n created = {2019-12-29T15:54:11.893Z},\n accessed = {2019-12-29},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2019-12-29T15:54:11.960Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Ravindranath, Arun and Devineni, Naresh and Lall, Upmanu and Cook, Edward R. and Pederson, Greg and Martin, Justin and Woodhouse, Connie},\n doi = {10.1029/2019WR024901},\n journal = {Water Resources Research},\n number = {9}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"beck-dejaeger-collin-tynes-effectofasyntheticfelinepheromoneformanagingunwantedscratching-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'beck-dejaeger-collin-tynes-effectofasyntheticfelinepheromoneformanagingunwantedscratching-2018', 'http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf', event);\">\n    Effect of a Synthetic Feline Pheromone for Managing Unwanted Scratching.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Beck,  A.; De Jaeger,  X.; Collin,  J.;  and Tynes,  V.\n</span>\n\n  \n\n</span>\n\n  <i>Intern J Appl Res Vet Med</i>, 16(1).  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf\"\n     onclick=\"log_download('beck-dejaeger-collin-tynes-effectofasyntheticfelinepheromoneformanagingunwantedscratching-2018', 'http://www.jarvm.com/articles/Vol16Iss1/Vol16 Iss1 Beck.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Effect of a Synthetic Feline Pheromone for Managing Unwanted Scratching [pdf]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/beck-dejaeger-collin-tynes-effectofasyntheticfelinepheromoneformanagingunwantedscratching-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('beck-dejaeger-collin-tynes-effectofasyntheticfelinepheromoneformanagingunwantedscratching-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@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 = {1fe94015-943a-3440-9216-7efe3025ba3e},\n created = {2018-04-10T13:57:02.281Z},\n accessed = {2018-04-07},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2018-04-10T13:57:02.281Z},\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}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  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</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"faruqui-alaeddini-jaramillo-potter-pugh-miningpatternsofcomorbidityevolutioninpatientswithmultiplechronicconditionsusingunsupervisedmultileveltemporalbayesiannetwork-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.plos.org/10.1371/journal.pone.0199768\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'faruqui-alaeddini-jaramillo-potter-pugh-miningpatternsofcomorbidityevolutioninpatientswithmultiplechronicconditionsusingunsupervisedmultileveltemporalbayesiannetwork-2018', 'http://dx.plos.org/10.1371/journal.pone.0199768', event);\">\n    Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Faruqui,  S., H., A.; Alaeddini,  A.; Jaramillo,  C., A.; Potter,  J., S.;  and Pugh,  M., J.\n</span>\n\n  \n\n</span>\n\n  <i>PLOS ONE</i>, 13(7): e0199768. 7 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.plos.org/10.1371/journal.pone.0199768\"\n     onclick=\"log_download('faruqui-alaeddini-jaramillo-potter-pugh-miningpatternsofcomorbidityevolutioninpatientswithmultiplechronicconditionsusingunsupervisedmultileveltemporalbayesiannetwork-2018', 'http://dx.plos.org/10.1371/journal.pone.0199768', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1371/journal.pone.0199768\"\n     onclick=\"log_download('faruqui-alaeddini-jaramillo-potter-pugh-miningpatternsofcomorbidityevolutioninpatientswithmultiplechronicconditionsusingunsupervisedmultileveltemporalbayesiannetwork-2018', 'http://doi.org/10.1371/journal.pone.0199768', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/faruqui-alaeddini-jaramillo-potter-pugh-miningpatternsofcomorbidityevolutioninpatientswithmultiplechronicconditionsusingunsupervisedmultileveltemporalbayesiannetwork-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('faruqui-alaeddini-jaramillo-potter-pugh-miningpatternsofcomorbidityevolutioninpatientswithmultiplechronicconditionsusingunsupervisedmultileveltemporalbayesiannetwork-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network},\n type = {article},\n year = {2018},\n pages = {e0199768},\n volume = {13},\n websites = {http://dx.plos.org/10.1371/journal.pone.0199768},\n month = {7},\n publisher = {Public Library of Science},\n day = {12},\n id = {de6ebdfd-6ec6-34fd-a603-6b2363ec936b},\n created = {2018-07-18T22:19:23.145Z},\n accessed = {2018-07-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2018-07-18T22:19:23.145Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely how multiple chronic conditions emerge among patients. We propose an unsupervised multi-level temporal Bayesian network to provide a compact representation of the relationship among emergence of multiple chronic conditions and patient level risk factors over time. To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages, starting with learning the inter-relationship of comorbidities within each year, followed by learning the intra-relationship of comorbidity emergence between consecutive years, and finally learning the hierarchical relationship of comorbidities and patient level risk factors. We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. Using a de-identified dataset of more than 250,000 patients receiving care from the U.S. Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised and semi-supervised learning approaches, as well as multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering focusing on traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), and back pain (BaPa). Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. These findings also revealed that the unsupervised approach has improved performance over multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.},\n bibtype = {article},\n author = {Faruqui, Syed Hasib Akhter and Alaeddini, Adel and Jaramillo, Carlos A. and Potter, Jennifer S. and Pugh, Mary Jo},\n editor = {Miller, Mark Webber},\n doi = {10.1371/journal.pone.0199768},\n journal = {PLOS ONE},\n number = {7}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Over the past few decades, the rise of multiple chronic conditions has become a major concern for clinicians. However, it is still not known precisely how multiple chronic conditions emerge among patients. We propose an unsupervised multi-level temporal Bayesian network to provide a compact representation of the relationship among emergence of multiple chronic conditions and patient level risk factors over time. To improve the efficiency of the learning process, we use an extension of maximum weight spanning tree algorithm and greedy search algorithm to study the structure of the proposed network in three stages, starting with learning the inter-relationship of comorbidities within each year, followed by learning the intra-relationship of comorbidity emergence between consecutive years, and finally learning the hierarchical relationship of comorbidities and patient level risk factors. We also use a longest path algorithm to identify the most likely sequence of comorbidities emerging from and/or leading to specific chronic conditions. Using a de-identified dataset of more than 250,000 patients receiving care from the U.S. Department of Veterans Affairs for a period of five years, we compare the performance of the proposed unsupervised Bayesian network in comparison with those of Bayesian networks developed based on supervised and semi-supervised learning approaches, as well as multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering focusing on traumatic brain injury (TBI), post-traumatic stress disorder (PTSD), depression (Depr), substance abuse (SuAb), and back pain (BaPa). Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. These findings also revealed that the unsupervised approach has improved performance over multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"fusterparra-tauler-bennasarveny-ligeza-lpezgonzlez-aguil-bayesiannetworkmodelingacasestudyofanepidemiologicsystemanalysisofcardiovascularrisk-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fuster-Parra,  P.; Tauler,  P.; Bennasar-Veny,  M.; Ligeza,  A.; López-González,  A., A.;  and Aguiló,  A.\n</span>\n\n  \n\n</span>\n\n  <i>Computer Methods and Programs in Biomedicine</i>, 126: 128-142. 4 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.cmpb.2015.12.010\"\n     onclick=\"log_download('fusterparra-tauler-bennasarveny-ligeza-lpezgonzlez-aguil-bayesiannetworkmodelingacasestudyofanepidemiologicsystemanalysisofcardiovascularrisk-2016', 'http://doi.org/10.1016/j.cmpb.2015.12.010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fusterparra-tauler-bennasarveny-ligeza-lpezgonzlez-aguil-bayesiannetworkmodelingacasestudyofanepidemiologicsystemanalysisofcardiovascularrisk-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fusterparra-tauler-bennasarveny-ligeza-lpezgonzlez-aguil-bayesiannetworkmodelingacasestudyofanepidemiologicsystemanalysisofcardiovascularrisk-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk},\n type = {article},\n year = {2016},\n keywords = {Bayesian networks,Cardiovascular lost years,Cardiovascular risk score,Causal dependency discovery,Metabolic syndrome,Model averaging},\n pages = {128-142},\n volume = {126},\n month = {4},\n publisher = {Elsevier Ireland Ltd},\n day = {1},\n id = {bb967501-eb4d-3400-802d-de3c68f37f48},\n created = {2020-03-17T22:32:56.551Z},\n accessed = {2020-03-17},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2020-03-17T22:32:56.657Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool.},\n bibtype = {article},\n author = {Fuster-Parra, P. and Tauler, P. and Bennasar-Veny, M. and Ligeza, A. and López-González, A. A. and Aguiló, A.},\n doi = {10.1016/j.cmpb.2015.12.010},\n journal = {Computer Methods and Programs in Biomedicine}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482512002065\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013', 'http://www.sciencedirect.com/science/article/pii/S0010482512002065', event);\">\n    Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Prestat,  E.; de Morais,  S., R.; Vendrell,  J., A.; Thollet,  A.; Gautier,  C.; Cohen,  P., A.;  and Aussem,  A.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in biology and medicine</i>, 43(4): 334-41. 5 2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482512002065\"\n     onclick=\"log_download('prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013', 'http://www.sciencedirect.com/science/article/pii/S0010482512002065', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours. [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2012.12.002\"\n     onclick=\"log_download('prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013', 'http://doi.org/10.1016/j.compbiomed.2012.12.002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('prestat-demorais-vendrell-thollet-gautier-cohen-aussem-learningthelocalbayesiannetworkstructurearoundtheznf217oncogeneinbreasttumours-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours.},\n type = {article},\n year = {2013},\n keywords = {Algorithms,Artificial Intelligence,Automation,Bayes Theorem,Breast Neoplasms,Breast Neoplasms: genetics,Breast Neoplasms: metabolism,Computer Simulation,Female,Gene Expression Profiling,Gene Expression Regulation, Neoplastic,Humans,Lipid Metabolism,Models, Genetic,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Oncogenes,Ovarian Neoplasms,Ovarian Neoplasms: genetics,Prognosis,Trans-Activators,Trans-Activators: genetics,Trans-Activators: metabolism},\n pages = {334-41},\n volume = {43},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482512002065},\n month = {5},\n id = {d5a3b899-1115-37e0-accc-99ea302c6cf1},\n created = {2015-04-12T20:17:32.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2017-03-14T14:39:16.063Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently amplified in breast and ovarian cancer, and correlated with shorter patient survival in these cancers. To properly address the difficulties in managing complex gene interactions given our limited sample, statistical significance of edge strengths was evaluated using bootstrapping and the less reliable edges were pruned to increase the network robustness. We found that 13 out of the 35 genes associated with deregulated ZNF217 expression in breast tumours have been previously associated with survival and/or prognosis in cancers. Identifying genes involved in lipid metabolism opens new fields of investigation to decipher the molecular mechanisms driven by the ZNF217 oncogene. Moreover, nine of the 13 genes have already been identified as putative ZNF217 targets by independent biological studies. We therefore suggest that the algorithms for inferring local BNs are valuable data mining tools for unraveling complex mechanisms of biological pathways from expression data. The source code is available at http://www710.univ-lyon1.fr/∼aaussem/Software.html.},\n bibtype = {article},\n author = {Prestat, Emmanuel and de Morais, Sérgio Rodrigues and Vendrell, Julie A and Thollet, Aurélie and Gautier, Christian and Cohen, Pascale A and Aussem, Alex},\n doi = {10.1016/j.compbiomed.2012.12.002},\n journal = {Computers in biology and medicine},\n number = {4}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this study, we discuss and apply a novel and efficient algorithm for learning a local Bayesian network model in the vicinity of the ZNF217 oncogene from breast cancer microarray data without having to decide in advance which genes have to be included in the learning process. ZNF217 is a candidate oncogene located at 20q13, a chromosomal region frequently amplified in breast and ovarian cancer, and correlated with shorter patient survival in these cancers. To properly address the difficulties in managing complex gene interactions given our limited sample, statistical significance of edge strengths was evaluated using bootstrapping and the less reliable edges were pruned to increase the network robustness. We found that 13 out of the 35 genes associated with deregulated ZNF217 expression in breast tumours have been previously associated with survival and/or prognosis in cancers. Identifying genes involved in lipid metabolism opens new fields of investigation to decipher the molecular mechanisms driven by the ZNF217 oncogene. Moreover, nine of the 13 genes have already been identified as putative ZNF217 targets by independent biological studies. We therefore suggest that the algorithms for inferring local BNs are valuable data mining tools for unraveling complex mechanisms of biological pathways from expression data. The source code is available at http://www710.univ-lyon1.fr/∼aaussem/Software.html.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2011\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2011')\"\n      onkeypress=\"toggleGroup('group_2011')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2011</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482511000825\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011', 'http://www.sciencedirect.com/science/article/pii/S0010482511000825', event);\">\n    Effective connectivity analysis of fMRI and MEG data collected under identical paradigms.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Plis,  S., M.; Weisend,  M., P.; Damaraju,  E.; Eichele,  T.; Mayer,  A.; Clark,  V., P.; Lane,  T.;  and Calhoun,  V., D.\n</span>\n\n  \n\n</span>\n\n  <i>Computers in Biology and Medicine</i>, 41(12): 1156-1165. 12 2011.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0010482511000825\"\n     onclick=\"log_download('plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011', 'http://www.sciencedirect.com/science/article/pii/S0010482511000825', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Effective connectivity analysis of fMRI and MEG data collected under identical paradigms [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compbiomed.2011.04.011\"\n     onclick=\"log_download('plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011', 'http://doi.org/10.1016/j.compbiomed.2011.04.011', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('plis-weisend-damaraju-eichele-mayer-clark-lane-calhoun-effectiveconnectivityanalysisoffmriandmegdatacollectedunderidenticalparadigms-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Effective connectivity analysis of fMRI and MEG data collected under identical paradigms},\n type = {article},\n year = {2011},\n keywords = {Bayesian networks,Effective connectivity,MEG,fMRI},\n pages = {1156-1165},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S0010482511000825},\n month = {12},\n id = {7d1289d2-7b8e-3052-836e-5cb4ddd3fb0a},\n created = {2015-04-12T20:17:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2017-03-14T14:39:16.063Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.},\n bibtype = {article},\n author = {Plis, Sergey M. and Weisend, Michael P. and Damaraju, Eswar and Eichele, Tom and Mayer, Andy and Clark, Vincent P. and Lane, Terran and Calhoun, Vince D.},\n doi = {10.1016/j.compbiomed.2011.04.011},\n journal = {Computers in Biology and Medicine},\n number = {12}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2003\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2003')\"\n      onkeypress=\"toggleGroup('group_2003')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2003</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046403001035\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003', 'http://www.sciencedirect.com/science/article/pii/S1532046403001035', event);\">\n    Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lee,  S.;  and Abbott,  P., A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Biomedical Informatics</i>, 36(4-5): 389-399. 8 2003.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1532046403001035\"\n     onclick=\"log_download('lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003', 'http://www.sciencedirect.com/science/article/pii/S1532046403001035', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2003.09.022\"\n     onclick=\"log_download('lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003', 'http://doi.org/10.1016/j.jbi.2003.09.022', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lee-abbott-bayesiannetworksforknowledgediscoveryinlargedatasetsbasicsfornurseresearchers-2003')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@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 = {2a30872d-864b-36ba-9a57-f71218d1cd44},\n created = {2015-04-12T19:14:39.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2017-03-14T14:39:16.063Z},\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}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  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</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_undefined\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_undefined')\"\n      onkeypress=\"toggleGroup('group_undefined')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>undefined</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis', 'http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf', event);\">\n    Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Venkatesh,  T.\n</span>\n\n  \n\n</span>\n\n  .  .\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf\"\n     onclick=\"log_download('venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis', 'http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis [pdf]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('venkatesh-applicationofartificialintelligencetobronchiectasispatientclinicaldataanalysis')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Application of Artificial Intelligence to Bronchiectasis Patient Clinical Data Analysis},\n type = {article},\n websites = {http://ircset.org/anand/2017papers/IRC-SET_2017_paper_P-5.pdf},\n id = {e35eda2f-e1b6-32f7-bb1c-75d3ef758b08},\n created = {2018-03-31T21:53:37.985Z},\n accessed = {2018-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2018-03-31T21:53:37.985Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {— The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.},\n bibtype = {article},\n author = {Venkatesh, Tejas}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  — The aim of the investigation was to analyse patient clinical data of Bronchiectasis patients in a Singaporean cohort in order to identify any unknown correlations between clinical parameters such as age, sex, presence of certain organisms in body etc. Both supervised learning and unsupervised machine learning computer algorithms were applied to the data set. A strong inverse correlation was found between the A.fumigatus burden level and P.aeruginosa status in the pulmonary track of patients. Differences in cytokine expressions in the body, which were found to be statistically significant, were also observed, and this could be used as a more accurate biomarker to flag Aspergillus as compared to the poorly accurate culture test which is currently used. Future research needs to be conducted to further investigate the relationship between A.fumigatus and P.aeruginosa and the viability of using the identified cytokines as biomarkers.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"nicholson-epidemiologicaldataminingofcardiovascularbayesiannetworks\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Epidemiological data mining of cardiovascular Bayesian networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Nicholson,  A.\n</span>\n\n  \n\n</span>\n\n   \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/nicholson-epidemiologicaldataminingofcardiovascularbayesiannetworks\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('nicholson-epidemiologicaldataminingofcardiovascularbayesiannetworks')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{\n title = {Epidemiological data mining of cardiovascular Bayesian networks},\n type = {misc},\n id = {0580e39e-0461-33e9-bd28-ea2af7142410},\n created = {2020-03-13T20:31:06.558Z},\n accessed = {2020-03-13},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {ad14e5fb-797f-35df-9a65-790d8a1f2cc8},\n last_modified = {2020-03-13T20:31:06.643Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {misc},\n author = {Nicholson, Ann}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);