PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classification of co-expressed genes from DNA regulatory regions
Giulio Pavesi and Giorgio Valentini
Information Fusion 2009.

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Abstract

The analysis of non--coding DNA regulatory regions is one of the most challenging open problems in computational biology. In this paper we investigate whether we can predict functional information about genes by using information extracted from their sequences together with expression data. We formalize this problem as a classification problem, and we apply Support Vector Machines (SVMs) with non linear kernels to predict classes of co-expressed genes obtained from clustering procedures. SVMs are trained using information about selected motifs extracted from DNA regulatory regions through combinatorial and statistical methods. In our experiments, we show that functional classes of genes can be predicted from biological sequence data in S. cerevisiae, achieving results competitive with those recently presented in the literature.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4309
Deposited By:Giorgio Valentini
Deposited On:13 March 2009

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