GFBA: A Biclustering Algorithm for Discovering Value-Coherent Biclusters

Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity of expression under different conditions. However, it is often the case that some genes behave similarly only on a subset of conditions and their behavior is uncorrelated over the rest of the conditions. As traditional clustering methods will fail to identify such gene groups, the biclustering paradigm is introduced recently to overcome this limitation. In contrast to traditional clustering, a biclustering method produces biclusters, each of which identifies a set of genes and a set of conditions under which these genes behave similarly. The boundary of a bicluster is usually fuzzy in practice as genes and conditions can belong to multiple biclusters at the same time but with different membership degrees. However, to the best of our knowledge, a method that can discover fuzzy value-coherent biclusters is still missing. In this paper, (i) we propose a new fuzzy bicluster model for value-coherent biclusters; (ii) based on this model, we define an objective function whose minimum will characterize good fuzzy value-coherent biclusters; and (iii) we propose a genetic algorithm based method, Genetic Fuzzy Biclustering Algorithm (GFBA), to identify fuzzy value-coherent biclusters. Our experiments show that GFBA is very efficient in converging to the global optimum.