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A methodology for biologically relevant pattern discovery from gene expression data

Contributo in Atti di convegno
Data di Pubblicazione:
2004
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.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
gene expression data analysis
Elenco autori:
R. G. Pensa; J. Besson; J-F. Boulicaut
Autori di Ateneo:
PENSA Ruggero Gaetano
Link alla scheda completa:
https://iris.unito.it/handle/2318/67779
Titolo del libro:
Discovery Science. DS 2004.
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
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URL

https://link.springer.com/chapter/10.1007/978-3-540-30214-8_18
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