@INPROCEEDINGS{Garcia_et.al._iea-aie10,
  author = {Beatriz García Jimenez and Agapito Ledezma and Araceli Sanchis.},
  title = {S.cerevisiae Complex Function Prediction with Modular Multi-Relational
	Framework.},
  booktitle = {Trends in Applied Intelligent Systems. Proceedings of the 23rd International
	Conference on Industrial, Engineering \& Other Applications of Applied
	Intelligent Systems (IEA-AIE 10).},
  year = {2010},
  editor = {Nicol\'{a}s Garc\'{\i}a-Pedrajas and Francisco Herrera and Colin
	Fyfe and Jos\'{e} Manuel Ben\'{\i}tez and Moonis Ali},
  volume = {6098},
  series = {Lecture Notes in Artificial Intelligence},
  pages = {82-91},
  month = {June},
  publisher = {Springer},
  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.},
  isbn = {978-3-642-13032-8},
  location = {Cordoba, Spain}
}

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Proceedings of the 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 10).","year":"2010","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":[]}],"volume":"6098","series":"Lecture Notes in Artificial Intelligence","pages":"82-91","month":"June","publisher":"Springer","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.","isbn":"978-3-642-13032-8","location":"Cordoba, Spain","bibtex":"@INPROCEEDINGS{Garcia_et.al._iea-aie10,\n author = {Beatriz García Jimenez and Agapito Ledezma and Araceli Sanchis.},\n title = {S.cerevisiae Complex Function Prediction with Modular Multi-Relational\n\tFramework.},\n booktitle = {Trends in Applied Intelligent Systems. Proceedings of the 23rd International\n\tConference on Industrial, Engineering \\& Other Applications of Applied\n\tIntelligent Systems (IEA-AIE 10).},\n year = {2010},\n editor = {Nicol\\'{a}s Garc\\'{\\i}a-Pedrajas and Francisco Herrera and Colin\n\tFyfe and Jos\\'{e} Manuel Ben\\'{\\i}tez and Moonis Ali},\n volume = {6098},\n series = {Lecture Notes in Artificial Intelligence},\n pages = {82-91},\n month = {June},\n publisher = {Springer},\n abstract = {Gene functions is an essential knowledge for understanding how metabolism\n\tworks and designing treatments for solving malfunctions. The Modular\n\tMulti-Relational Framework (MMRF) is able to predict gene group functions.\n\tSince genes working together, it is focused on group functions rather\n\tthan isolated gene functions. The approach of MMRF is flexible in\n\tseveral aspects, such as the kind of groups, the integration of different\n\tdata sources, the organism and the knowledge representation. Besides,\n\tthis framework takes advantages of the intrinsic relational structure\n\tof biological data, giving an easily biological interpretable and\n\tunique relational decision tree predicting N functions at once. This\n\tresearch work presents a group function prediction of S.cerevisiae\n\t(i.e.Yeast) genes grouped by protein complexes using MMRF. The results\n\tshow that the predictions are restricted by the shortage of examples\n\tper class. Also, they assert that the knowledge representation is\n\tvery determinant to exploit the available relational information\n\trichness, and therefore, to improve both the quantitative results\n\tand their biological interpretability.},\n isbn = {978-3-642-13032-8},\n location = {Cordoba, Spain}\n}\n\n","author_short":["Jimenez, B. G.","Ledezma, A.","Sanchis., A."],"editor_short":["García-Pedrajas, N.","Herrera, F.","Fyfe, C.","Benítez, J. 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