Identification of Biclusters in Huntington’s Disease Dataset Using a New Variant of Grey Wolf Optimizer

Biclustering is a useful technique to identify subgroups of genes that have same type of expression characteristics with respect to some conditions in microarray gene expression data. This is a complex problem where meta-heuristic algorithms are more suitable to explore the large datasets for finding biclusters of optimal quality. In this paper, there is an attempt for the first time to choose biclusters with respect to shifting and scaling behaviors using Huntington's disease database applying Grey Wolf Optimizer (GWO) along with its proposed modified version namely, Enhanced Search Grey Wolf Optimizer (ES-GWO). ES-GWO incorporates strategies that make the search process more balanced with respect to exploration and exploitation compared to the state-of-the-art techniques (GWO, RM-GWO). The efficacy of ES-GWO is validated on several benchmark instances and compared with the existing meta-heuristic techniques (PSO, HS, Firefly, ABC and DE) based on convergence quality. Finally, from 100 biclusters produced by ES-GWO top 5 were separated. 7 genes common in those 5 biclusters have proved to be biologically significant.