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In Silico Biol. 2008;8(2):157-75.

Constraint-based knowledge discovery from SAGE data.

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GREYC, CNRS UMR 6072, Université de Caen, Campus Côte de Nacre, F-14032 Caen Cédex, France.


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.

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