S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework. García-Jiménez, B., Ledezma Espino, A., & Sanchis de Miguel, A. In Trends in Applied Intelligent Systems: Proceedings of the 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 10), volume 6098, of Lecture Notes in Artificial Intelligence, pages 82-91, 6, 2010. Springer.
S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework [link]Website  abstract   bibtex   
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.
@inproceedings{
 title = {S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework},
 type = {inproceedings},
 year = {2010},
 identifiers = {[object Object]},
 pages = {82-91},
 volume = {6098},
 websites = {http://link.springer.com/chapter/10.1007%2F978-3-642-13033-5_9},
 month = {6},
 publisher = {Springer},
 city = {Cordoba},
 series = {Lecture Notes in Artificial Intelligence},
 id = {0b923e30-1c77-32f7-9673-9c29a7af47c9},
 created = {2014-03-27T15:09:26.000Z},
 file_attached = {false},
 profile_id = {fa910c8b-8889-3a42-afc9-302da7e3933a},
 group_id = {ff1f9038-dd83-321a-9605-910d757253bb},
 last_modified = {2017-03-14T12:53:44.413Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 country = {Spain},
 private_publication = {false},
 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.},
 bibtype = {inproceedings},
 author = {García-Jiménez, Beatriz and Ledezma Espino, Agapito and Sanchis de Miguel, Araceli},
 booktitle = {Trends in Applied Intelligent Systems: Proceedings of the 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 10)}
}

Downloads: 0