The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to evaluate the data and to formulate new biological hypotheses. To this purpose, co-clustering techniques are widely used: these identify groups of genes that show similar activity patterns under a specific subset of the experimental conditions by measuring the similarity in expression within these groups. However, in many applications, distance metrics based only on expression levels fail in capturing biologically meaningful clusters. We propose a methodology in which a standard expression-based co-clustering algorithm is enhanced by sets of constraints which take into account the similarity/dissimilarity (inferred by the Gene Ontology, GO) between pairs of genes. Our approach minimizes the intervention of the analyst within the co-clustering process. It provides meaningful co-clusters whose discovery and interpretation is increased by embedding GO annotations.

Ontology-driven Co-clustering of Gene Expression Data

CORDERO, Francesca;PENSA, Ruggero Gaetano;VISCONTI, ALESSIA;IENCO, Dino;BOTTA, Marco
2009

Abstract

The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to evaluate the data and to formulate new biological hypotheses. To this purpose, co-clustering techniques are widely used: these identify groups of genes that show similar activity patterns under a specific subset of the experimental conditions by measuring the similarity in expression within these groups. However, in many applications, distance metrics based only on expression levels fail in capturing biologically meaningful clusters. We propose a methodology in which a standard expression-based co-clustering algorithm is enhanced by sets of constraints which take into account the similarity/dissimilarity (inferred by the Gene Ontology, GO) between pairs of genes. Our approach minimizes the intervention of the analyst within the co-clustering process. It provides meaningful co-clusters whose discovery and interpretation is increased by embedding GO annotations.
11th Conference of the Italian Association for Artificial Intelligence AI*IA 2009
Reggio Emilia, Italy
December 9-12, 2009
11th International Conference of the Italian Association for Artificial Intelligence: Emergent Perspectives in Artificial Intelligence, AI IA 2009
SPRINGER-VERLAG
5883/2009
426
435
9783642102905
http://www.aixia09.unimore.it/
F. Cordero; R. G. Pensa; A. Visconti; D. Ienco; M. Botta
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/67372
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