Exploring patterns of epigenetic information with data mining techniques

Vanessa Aguiar-Pulido, José A. Seoane, Marcos Gestal, Julián Dorado

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)779-789
Number of pages11
JournalCurrent pharmaceutical design
Volume19
Issue number4
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Bioinformatics
  • Data mining
  • Epigenetics
  • Knowledge discovery

ASJC Scopus subject areas

  • Pharmacology
  • Drug Discovery

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