A Parallel Algorithm for Gene Expressing Data Biclustering

Abstract

Biclustering of the gene expressing data is an important task in bioinformatics. By clustering the gene expressing data obtained under different experimental conditions, function and regulatory elements of the gene sequence can be analyzed and recognized. A parallel biclustering algorithm for gene expressing data is presented. Based on the anti-monotones property of the quality of the data sets with their sizes, the algorithm starts from the data sets containing of all the 2*2 submatrices of the gene expressing data matrix, and gets the final biclusters by gradually adding columns and rows on the data sets. Experimental results show that our algorithm has superiority over other similar algorithms in terms of processing speedup and quality of clustering and efficiency.