@inproceedings{Garcia_et.al._iea-aie10,
  author    = {Beatriz Garc\'{\i}a-Jim{\'e}nez and
               Agapito Ledezma and
               Araceli Sanchis},
  title     = {S.cerevisiae Complex Function Prediction with Modular Multi-Relational
               Framework},
  booktitle = {Trends in Applied Intelligent Systems - 23rd International
               Conference on Industrial Engineering and Other Applications
               of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain,
               June 1-4, 2010, Proceedings, Part III},
  year      = {2010},
  pages     = {82-91},
  abstract  = {Gene functions is an essential knowledge for understanding how metabolism works and designing treatments for solving malfunctions. The Modular Multi-Relational Framework (MMRF) is able to predict gene group functions. Since genes working together, it is focused on group functions rather than isolated gene functions. The approach of MMRF is flexible in several aspects, such as the kind of groups, the integration of different data sources, the organism and the knowledge representation. Besides, this framework takes advantages of the intrinsic relational structure of biological data, giving an easily biological interpretable and unique relational decision tree predicting N functions at once.
This research work presents a group function prediction of S.cerevisiae (i.e.Yeast) genes grouped by protein complexes using MMRF. The results show that the predictions are restricted by the shortage of examples per class. Also, they assert that the knowledge representation is very determinant to exploit the available relational information richness, and therefore, to improve both the quantitative results and their biological interpretability.},
  ee        = {http://dx.doi.org/10.1007/978-3-642-13033-5_9},
  publisher = {Springer},
  series    = {Lecture Notes in Artificial Intelligence},
  volume    = {6098},
  editor    = {Nicol\'{a}s Garc\'{\i}a-Pedrajas and Francisco Herrera and Colin Fyfe and Jos\'{e} Manuel Ben\'{\i}tez and Moonis Ali},
  isbn      = {978-3-642-13032-8}
}

{"_id":{"_str":"5342a9600e946d920a002f9a"},"__v":1,"authorIDs":[],"author_short":["García-Jiménez, B.","Ledezma, A.","Sanchis, A."],"bibbaseid":"garcajimnez-ledezma-sanchis-scerevisiaecomplexfunctionpredictionwithmodularmultirelationalframework-2010","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Beatriz"],"propositions":[],"lastnames":["García-Jiménez"],"suffixes":[]},{"firstnames":["Agapito"],"propositions":[],"lastnames":["Ledezma"],"suffixes":[]},{"firstnames":["Araceli"],"propositions":[],"lastnames":["Sanchis"],"suffixes":[]}],"title":"S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework","booktitle":"Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part III","year":"2010","pages":"82-91","abstract":"Gene functions is an essential knowledge for understanding how metabolism works and designing treatments for solving malfunctions. The Modular Multi-Relational Framework (MMRF) is able to predict gene group functions. Since genes working together, it is focused on group functions rather than isolated gene functions. The approach of MMRF is flexible in several aspects, such as the kind of groups, the integration of different data sources, the organism and the knowledge representation. Besides, this framework takes advantages of the intrinsic relational structure of biological data, giving an easily biological interpretable and unique relational decision tree predicting N functions at once. This research work presents a group function prediction of S.cerevisiae (i.e.Yeast) genes grouped by protein complexes using MMRF. The results show that the predictions are restricted by the shortage of examples per class. Also, they assert that the knowledge representation is very determinant to exploit the available relational information richness, and therefore, to improve both the quantitative results and their biological interpretability.","ee":"http://dx.doi.org/10.1007/978-3-642-13033-5_9","publisher":"Springer","series":"Lecture Notes in Artificial Intelligence","volume":"6098","editor":[{"firstnames":["Nicolás"],"propositions":[],"lastnames":["García-Pedrajas"],"suffixes":[]},{"firstnames":["Francisco"],"propositions":[],"lastnames":["Herrera"],"suffixes":[]},{"firstnames":["Colin"],"propositions":[],"lastnames":["Fyfe"],"suffixes":[]},{"firstnames":["José","Manuel"],"propositions":[],"lastnames":["Benítez"],"suffixes":[]},{"firstnames":["Moonis"],"propositions":[],"lastnames":["Ali"],"suffixes":[]}],"isbn":"978-3-642-13032-8","bibtex":"@inproceedings{Garcia_et.al._iea-aie10,\r\n author = {Beatriz Garc\\'{\\i}a-Jim{\\'e}nez and\r\n Agapito Ledezma and\r\n Araceli Sanchis},\r\n title = {S.cerevisiae Complex Function Prediction with Modular Multi-Relational\r\n Framework},\r\n booktitle = {Trends in Applied Intelligent Systems - 23rd International\r\n Conference on Industrial Engineering and Other Applications\r\n of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain,\r\n June 1-4, 2010, Proceedings, Part III},\r\n year = {2010},\r\n pages = {82-91},\r\n abstract = {Gene functions is an essential knowledge for understanding how metabolism works and designing treatments for solving malfunctions. The Modular Multi-Relational Framework (MMRF) is able to predict gene group functions. Since genes working together, it is focused on group functions rather than isolated gene functions. The approach of MMRF is flexible in several aspects, such as the kind of groups, the integration of different data sources, the organism and the knowledge representation. Besides, this framework takes advantages of the intrinsic relational structure of biological data, giving an easily biological interpretable and unique relational decision tree predicting N functions at once.\r\nThis research work presents a group function prediction of S.cerevisiae (i.e.Yeast) genes grouped by protein complexes using MMRF. The results show that the predictions are restricted by the shortage of examples per class. Also, they assert that the knowledge representation is very determinant to exploit the available relational information richness, and therefore, to improve both the quantitative results and their biological interpretability.},\r\n ee = {http://dx.doi.org/10.1007/978-3-642-13033-5_9},\r\n publisher = {Springer},\r\n series = {Lecture Notes in Artificial Intelligence},\r\n volume = {6098},\r\n editor = {Nicol\\'{a}s Garc\\'{\\i}a-Pedrajas and Francisco Herrera and Colin Fyfe and Jos\\'{e} Manuel Ben\\'{\\i}tez and Moonis Ali},\r\n isbn = {978-3-642-13032-8}\r\n}\r\n\r\n\r\n","author_short":["García-Jiménez, B.","Ledezma, A.","Sanchis, A."],"editor_short":["García-Pedrajas, N.","Herrera, F.","Fyfe, C.","Benítez, J. M.","Ali, M."],"key":"Garcia_et.al._iea-aie10","id":"Garcia_et.al._iea-aie10","bibbaseid":"garcajimnez-ledezma-sanchis-scerevisiaecomplexfunctionpredictionwithmodularmultirelationalframework-2010","role":"author","urls":{"Link":"http://dx.doi.org/10.1007/978-3-642-13033-5_9"},"downloads":0,"html":"","metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"http://www.caos.inf.uc3m.es/bibs/pubCAOS.bib","downloads":0,"keywords":[],"search_terms":["cerevisiae","complex","function","prediction","modular","multi","relational","framework","garcía-jiménez","ledezma","sanchis"],"title":"S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework","year":2010,"dataSources":["Z4T4fewybNpYCWveD"]}