Bagging for biclustering: application to microarray data

Authors:
Blaise Hanczar;Mohamed Nadif
Affiliations:
LIPADE, University Paris Descartes, Paris, France;LIPADE, University Paris Descartes, Paris, France
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Year:
2010

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Abstract

One of the major tools of transcriptomics is the biclustering that simultaneously constructs a partition of both examples and genes. Several methods have been proposed for microarray data analysis that enables to identify groups of genes with similar expression pro?les only under a subset of examples. We propose to improve the quality of these biclustering methods by adapting the approach of bagging to biclustering problems. The principle consists in generating a set of biclusters and aggregating the results. Our method has been tested with success on artificial and real datasets.