title: Exploring patterns of epigenetic information with data mining techniques.
abstract: Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.
authors.affiliation: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, S/N, 15071 A Coruña, Spain. vaguiar@udc.es
authors.forename: Vanessa
authors.initials: V
authors.lastname: Aguiar-Pulido
authors.forename: José A
authors.initials: JA
authors.lastname: Seoane
authors.forename: Marcos
authors.initials: M
authors.lastname: Gestal
authors.forename: Julián
authors.initials: J
authors.lastname: Dorado
mesh_terms: D000375:Aging; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D044127:Epigenesis, Genetic; D015870:Gene Expression; D055106:Genome-Wide Association Study / Q000379:methods; D057166:High-Throughput Screening Assays / Q000379:methods; D006801:Humans; D009154:Mutation