S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework

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 expl...

• Type: Journal
• Year: 2010
• Where: IEAAIE
• Authors: Beatriz García Jiménez, Agapito Ledezma, Araceli Sanchís
• Keywords: Artificial Intelligence, Gene Functions, Group Function, IEAAIE 2010, Knowledge Representation