Nicolas PASQUIER ♦ Université Côte d'Azur

CGGA

Co-expressed Gene Groups Analysis

Description

Implementation of the CGGA algorithm for extracting bi-clusters of co-regulated genes from integrated gene expression data and gene annotations obtained from biological knowledge.

First, the gene rank hierarchy is constructed using SAM F-Scores and then, the CGGA algorithm is applied to group co-expressed genes.

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Reference

Co-expressed Gene Groups Analysis (CGGA): An automatic tool for the interpretation of microarray experiments, Ricardo Martinez, Nicolas Pasquier, Claude Pasquier, Martine Collard and Lucero Lopez-Perez, Journal of Integrative Bioinformatics, 3:2(1-12), 2006.

Experimental Results

These results were obtained from the integrated Derisi et al. dataset containing 2465 Yeast gene expression measurements for 7 biological conditions and gene annotations.

Related Publications

Analyse des groupes de gènes co-exprimés : un outil automatique pour l'interprétation des expériences de biopuces, Ricardo Martinez, Nicolas Pasquier, Claude Pasquier, Martine Collard and Lucero Lopez-Perez, Revue des Nouvelles Technologies de l'Information, Classification : points de vue croisés, 2008. 

Knowledge integration models for mining gene expression data, Ricardo Martinez, PhD Thesis, Université de Nice Sophia Antipolis, 2007.

Interpreting microarray experiments via co-expressed gene groups analysis, Ricardo Martinez, Nicolas Pasquier, Claude Pasquier and Lucero Lopez-Perez, Proceedings of the DS international conference on Discovery Science, pages 316-320, Springer-Verlag, 2006.

Analyse des groupes de gènes co-exprimés (AGGC) : un outil automatique pour l'interpretation des expériences de biopuces, Ricardo Martinez, Nicolas Pasquier, Martine Collard, Lucero Lopez-Perez and Claude Pasquier, Actes des XIIIème Rencontres de la Société Française de Classification, Université de Metz et LITA, 2006.