Mining deterministic biclusters in gene expression data

Abstract

A bicluster of a gene expression dataset captures the coherence of a subset of genes and a subset of conditions. Biclustering algorithms are used to discover biclusters whose subset of genes are co-regulated under subset of conditions. In this paper, we present a novel approach, called DBF (deterministic biclustering with frequent pattern mining) to finding biclusters. Our scheme comprises two phases. In the first phase, we generate a set of good quality biclusters based on frequent pattern mining. In the second phase, the biclusters are further iteratively refined (enlarged) by adding more genes and/or conditions. We evaluated our scheme against FLOC and our results show that DBF can generate larger and better biclusters.