A novel biclustering approach with iterative optimization to analyze gene expression data

作者：Sutheeworapong S; Ota M; Ohta H; Kinoshita K

来源：Advances and Applications in Bioinformatics and Chemistry, 2012.

DOI：http://dx.doi.org/10.2147/AABC.S32622

• biclustering
• microarray data
• genetic algorithm
• Pearson&rsquo
• s correlation coefficient

novel biclustering approach with iterative optimization to analyze gene expression data Methodology (2345) Total Article Views Authors: Sutheeworapong S, Ota M, Ohta H, Kinoshita K Video abstract presented by Sawannee Sutheeworapong. Views: 130 Published Date September 2012 Volume 2012:5 Pages 23 - 59 DOI: http://dx.doi.org/10.2147/AABC.S32622 Received: 04 April 2012 Accepted: 09 May 2012 Published: 07 September 2012 Sawannee Sutheeworapong,1,2 Motonori Ota,4 Hiroyuki Ohta,1 Kengo Kinoshita2,3 1Department of Biological Sciences, Graduate School of Biosciences and Biotechnology, Tokyo Institute of Technology, Tokyo, Japan; 2Graduate School of Information Sciences, 3Institute of Development, Aging and Cancer, Tohoku University, Miyagi, Japan; 4Graduate School of Information Sciences, Nagoya University, Nagoya, Japan Objective: With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically. Results: We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson＊s correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented. Conclusion: BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms.

• 出版日期2012