A Comparison of Biclustering with Clustering Algorithms

Abstract

In the past years, microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern plays an important role in the analysis of genes. Besides traditional clustering methods, biclustering is also being used to analyze biological datasets due to its ability to group both genes across conditions simultaneously. The paper presents a comparison of advanced with the traditional tools for biological data extraction. This paper compares different clustering and biclustering approaches used to analyze DLBCL (diffuse large B-cell lymphoma) microarray dataset. The algorithms were compared on the grounds of enrichment values with support from runtime analysis. Typical annotations for the analyzed list of genes can be well understood using the BicAT toolbox. The paper explains in detail the intellects affecting the enrichment values, leading to the best technique for the dataset mentioned above.