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.

A methodology for biologically relevant pattern discovery from gene expression data

PENSA, Ruggero Gaetano;
2004-01-01

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.
2004
7th International Conference on Discovery Science DS 2004
Padova, Italy
October 2-5, 2004
Discovery Science. DS 2004.
Springer
3245
230
241
978-3-540-23357-2
978-3-540-30214-8
https://link.springer.com/chapter/10.1007/978-3-540-30214-8_18
gene expression data analysis
R. G. Pensa; J. Besson; J-F. Boulicaut
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/67779
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