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DIS
2004
Springer

A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data

14 years 5 months ago
A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data
Abstract. One of the most exciting scientific challenges in functional genomics concerns the discovery of biologically relevant patterns from gene expression data. For instance, it is extremely useful to provide putative synexpression groups or transcription modules to molecular biologists. We propose a methodology that has been proved useful in real cases. It is described as a prototypical KDD scenario which starts from raw expression data selection until useful patterns are delivered. Our conceptual contribution is (a) to emphasize how to take the most from recent progress in constraint-based mining of set patterns, and (b) to propose a generic approach for gene expression data enrichment. The methodology has been validated on real data sets.
Ruggero G. Pensa, Jérémy Besson, Jea
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where DIS
Authors Ruggero G. Pensa, Jérémy Besson, Jean-François Boulicaut
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