Classification of co-expressed genes from DNA regulatory regions

Authors:
Giulio Pavesi;Giorgio Valentini
Affiliations:
Dipartimento di Scienze Biomolecolarie Biotecnologie, Universití degli Studi di Milano, Via Celoria 20, Milano, Italy;Dipartimento di Scienze dell'Informazione, Universití degli Studi di Milano, Via Comelico 39, 20135 Milano, Italy
Venue:
Information Fusion
Year:
2009

Citing 2
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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 Saccharomices cerevisiae, achieving results competitive with those recently presented in the literature.