Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). In this paper we exploit a multivariate approach, based on graphical models, to deal with gene sets defined by pathways. Given a sample of microarray data corresponding to two experimental conditions and a pathway linking some of the genes, we investigate whether the strength of the relations induced by the functional links change among the two experimental conditions.

A graphical models approach for comparing gene sets

CHIOGNA M.;
2010

Abstract

Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). In this paper we exploit a multivariate approach, based on graphical models, to deal with gene sets defined by pathways. Given a sample of microarray data corresponding to two experimental conditions and a pathway linking some of the genes, we investigate whether the strength of the relations induced by the functional links change among the two experimental conditions.
Complex data modeling and computationally intensive statistical methods
115
122
MASSA M.S; CHIOGNA M.; ROMUALDI C
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/647137
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