Bagging for Biclustering: Application to Microarray Data

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.