Biclustering Algorithms for Microarray Data

Scientific Essay from the year 2017 in the subject Computer Science - Bioinformatics, grade: 3, , language: English, abstract: This survey aims to analyze a large number of existing methods to biclustering, and classify them in accordance with the methods used to perform the search. DNA microarray aims at extracting useful information that can be applied in medical and biological studies. Clustering is one of the most utilized data mining technique to analyze the gene expression data. Generally, there are subsets of genes that behave similarily or under subsets of conditions. Therefore, biclustering is introduced to identify the subgroups of genes and subgroups of conditions by performing simultaneous clustering of both rows and columns of the gene expression matrix, rather than clustering these two dimensions separately. DNA microarray technologies have made it feasible to monitor transcription levels of tens of thousands of genes in a single expriement. A typical DNA microarray experiment involves a multistep procedure: fabrication of microarrays by fixing properly designed oligonucleotides representing specific genes; hybridization of cDNA populations onto the microarray; scanning hybridization signals and image analysis; transformation and normalization of data; and analyzing data to identify differentially expressed genes as well as sets of genes that are co-regulated.

Balamurugan Rengeswaran received the B.E. degree in Computer Science and Engineering in 2010 from the Government College of Engineering, Salem and the M.E. degree in Computer Science and Engineering in 2012 from the Bannari Amman Institute of Technology, Sathyamangalam. He is completed his Ph.D in Information and Communication Engineering in 2016 from Anna University, Chennai. Currently he is working as an Associate Professor in Department of Computer Science and Engineering in Vellore Institute of Technology, Vellore. He has published more than 20 papers in various international journals and conferences. His areas of interest include data mining and meta-heuristic optimization techniques.