title: Machine learning in bioinformatics.
abstract: This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.
authors.affiliation: Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizabal, 1, 20018 San Sebastian, Spain. pedro.larranaga@ehu.es
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga
authors.forename: Borja
authors.initials: B
authors.lastname: Calvo
authors.forename: Roberto
authors.initials: R
authors.lastname: Santana
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.forename: Josu
authors.initials: J
authors.lastname: Galdiano
authors.forename: Iñaki
authors.initials: I
authors.lastname: Inza
authors.forename: José A
authors.initials: JA
authors.lastname: Lozano
authors.forename: Rubén
authors.initials: R
authors.lastname: Armañanzas
authors.forename: Guzmán
authors.initials: G
authors.lastname: Santafé
authors.forename: Aritz
authors.initials: A
authors.lastname: Pérez
authors.forename: Victor
authors.initials: V
authors.lastname: Robles
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D023281:Genomics; D008962:Models, Theoretical; D040901:Proteomics