title: Inference system using softcomputing and mixed data applied in metabolic pathway datamining.
abstract: This paper describes the development of an inference system used for the identification of genes that encode enzymes of metabolic pathways. Input sequence alignment values are used to classify the best candidate genes for inclusion in a metabolic pathway map. The system workflow allows the user to provide feedback, which is stored in conjunction with analysed sequences for periodic retraining. The construction of the system involved the study of several different classifiers with various topologies, data sets and parameter normalisation data models. Experimental results show an excellent prediction capability with the classifiers trained with mixed data providing the best results.
authors.affiliation: Departamento de Electrónica, Universidad Técnica Federico Santa María, Avda España 1680, Valparaiso, 2340000, Chile. tomas.arredondo@usm.cl
authors.forename: Tomás
authors.initials: T
authors.lastname: Arredondo
authors.forename: Diego
authors.initials: D
authors.lastname: Candel
authors.forename: Mauricio
authors.initials: M
authors.lastname: Leiva
authors.forename: Lioubov
authors.initials: L
authors.lastname: Dombrovskaia
authors.forename: Loreine
authors.initials: L
authors.lastname: Agulló
authors.forename: Michael
authors.initials: M
authors.lastname: Seeger
mesh_terms: D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D016415:Sequence Alignment; D014584:User-Computer Interface