Modular Multi-Relational Framework for Gene Group Function Prediction. Jiménez, B. G., Ledezma, A., & Sanchis, A. In Presented at the 19th International Conference on Inductive Logic Programming Conference, ILP 2009, Leuven, Belgium, July, 2011. Poster.
Modular Multi-Relational Framework for Gene Group Function Prediction [link]Paper  abstract   bibtex   
Determining the functions of genes is essential for understanding how the metabolisms work, and for trying to solve their malfunctions. Genes usually work in groups rather than isolated, so functions should be assigned to gene groups and not to individual genes. Moreover, the genetic knowledge has many relations and is very frequently changeable. Thus, a propositional ad-hoc approach is not appropriate to deal with the gene group function prediction domain. We propose the Modular Multi-Relational Framework (MMRF), which faces the problem from a relational and flexible point of view. The MMRF consists of several modules covering all involved domain tasks (grouping, representing and learning using computational prediction techniques). A specific application is described, including a relational representation language, where each module of MMRF is individually instantiated and refined for obtaining a prediction under specific given conditions.
@inproceedings{ Garcia_et.al._ilp09,
  author = {Beatriz Garc\ıa Jiménez and Agapito Ledezma and Araceli Sanchis},
  title = {Modular Multi-Relational Framework for Gene Group Function Prediction},
  abstract = {Determining the functions of genes is essential for understanding how the metabolisms work, and for trying to solve their malfunctions. Genes usually work in groups rather than isolated, so functions should be assigned to gene groups and not to individual genes. Moreover, the genetic knowledge has many relations and is very frequently changeable. Thus, a propositional ad-hoc approach is not appropriate to deal with the gene group function prediction domain. We propose the Modular Multi-Relational Framework (MMRF), which faces the problem from a relational and flexible point of view. The MMRF consists of several modules covering all involved domain tasks (grouping, representing and learning using computational prediction techniques). A specific application is described, including a relational representation language, where each module of MMRF is individually instantiated and refined for obtaining a prediction under specific given conditions.},
  booktitle = {Presented at the 19th International Conference on Inductive Logic Programming Conference, ILP 2009, Leuven, Belgium, July, 2011. Poster},
  url = {http://www.cs.kuleuven.be/~dtai/ilp-mlg-srl/dokuwiki/doku.php?id=paper:ilp:52}
}
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