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.
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Communication dans un congrès
7th International Conference on Discovery Science, DS 2004, Oct 2004, Padova, Italy. Springer Verlag, pp.230-241, 2004, 〈10.1007/978-3-540-30214-8_18〉
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https://hal.archives-ouvertes.fr/hal-01588193
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Soumis le : vendredi 15 septembre 2017 - 11:48:29
Dernière modification le : jeudi 19 avril 2018 - 14:38:03

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Ruggero Pensa, Jérémy Besson, Jean-François Boulicaut. A methodology for biologically relevant pattern discovery from gene expression data. 7th International Conference on Discovery Science, DS 2004, Oct 2004, Padova, Italy. Springer Verlag, pp.230-241, 2004, 〈10.1007/978-3-540-30214-8_18〉. 〈hal-01588193〉

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