Affiliations: GREYC, CNRS UMR 6072, Université de Caen, Campus
Côte de Nacre, F-14032 Caen Cédex, France | Université de Lyon, Lyon, F-69003, France;
Université Lyon 1, Lyon, F-69003, France; CNRS, UMR5534, Centre de
génétique moléculaire et cellulaire, Villeurbanne, F-69622,
France | Department of Cybernetics, Czech Technical University
in Prague, Technická 2, Prague 166 27, Czech Republic | LI, Université de Tours, Place Jean Jaurès,
F-41029 Blois, France
Abstract: Current analyses of co-expressed genes are often based on global
approaches such as clustering or bi-clustering. An alternative way is to employ
local methods and search for patterns – sets of genes displaying specific
expression properties in a set of situations. The main bottleneck of this type
of analysis is twofold – computational costs and an overwhelming number of
candidate patterns which can hardly be further exploited. A timely application
of background knowledge available in literature databases, biological
ontologies and other sources can help to focus on the most plausible patterns
only. The paper proposes, implements and tests a flexible constraint-based
framework that enables the effective mining and representation of meaningful
over-expression patterns representing intrinsic associations among genes and
biological situations. The framework can be simultaneously applied to a wide
spectrum of genomic data and we demonstrate that it allows to generate new
biological hypotheses with clinical implications.
