NBF: An FCA-Based Algorithm to Identify Negative Correlation Biclusters of DNA Microarray Data

Abstract

Biclustering is a popular technique to study gene expression data, especially to identify functionally related groups of genes under subsets of conditions. Nevertheless, most of the existing biclustering algorithms only focus on the positive correlations of genes. However, recent research shows that groups of biologically significant genes may exhibit negative correlations. Thus, we need a novel way to efficiently unveil such a type of correlations. We introduce, in this paper, a new algorithm, called the Negative Bicluster Finder (NBF). The sighting features of the NBF stands in its ability to discover the biclusters of negative correlations using the theoretical results provided by the Formal Concept Analysis. Exhaust experiments are carried out on three real-life datasets to assess the performance of the NBF. Our results prove the NBF's ability to statistically and biologically identify significant biclusters.