A Biclustering Based Classification Framework for Cancer Diagnosis and Prognosis

In gene expression microarray data analysis, biclustering has been demonstrated to be one of the most effective methods for discovering gene ex- pression patterns under various conditions. We present in this study a framework to take advantage of the homogeneously expressed genes in biclusters to construct a classifier for sample class membership prediction. Extensive experiments on 8 real cancer microarray datasets (4 diagnostic and 4 prognostic) show that our pro- posed classifier performed superior in both cancer diagnosis and prognosis, the latter of which was regarded quite difficult previously. Additionally, our results demonstrate that sample classification accuracy can serve as a good subjective quality measure for biclusters.