García Jiménez, BeatrizLedezma Espino, Agapito IsmaelSanchis de Miguel, María Araceli2010-12-162010-12-16200919th International Conference on Inductive Logic Programming, ILP 2009, p. 1-6.https://hdl.handle.net/10016/9784Poster of: 19th International Conference on Inductive Logic Programming (ILP 2009), Leuven, Belgium, 2 - 4 Jul, 2009Determining 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.application/pdftext/plainengMulti relational data miningGene functionMulti-label relational decision treeInductive logic programmingStructure datalectureInformáticaopen access1619th International Conference on Inductive Logic Programming, ILP 2009