Pattern recognition in biological time series

Authors:
Francisco Gómez-Vela;Francisco Martínez-Álvarez;Carlos D. Barranco;Norberto Díaz-Díaz;Domingo Savio Rodríguez-Baena;Jesús S. Aguilar-Ruiz
Affiliations:
Department of Computer Science, Pablo de Olavide University of Seville;Department of Computer Science, Pablo de Olavide University of Seville;Department of Computer Science, Pablo de Olavide University of Seville;Department of Computer Science, Pablo de Olavide University of Seville;Department of Computer Science, Pablo de Olavide University of Seville;Department of Computer Science, Pablo de Olavide University of Seville
Venue:
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Year:
2011

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Abstract

Knowledge extraction from gene expression data has been one of the main challenges in the bioinformatics field during the last few years. In this context, a particular kind of data, data retrieved in a temporal basis (also known as time series), provide information about the way a gene can be expressed during time. This work presents an exhaustive analysis of last proposals in this area, particularly focusing on those proposals using non-supervised machine learning techniques (i.e. clustering, biclustering and regulatory networks) to find relevant patterns in gene expression.