Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data

Abstract

DNA microarray gene expression data analysis has provided new insights into gene function, disease pathophysiology, disease classification, and drug development. Biclustering in gene expression data is a subset of the genes demonstrating consistent patterns over a subset of the conditions. The proposed work finds the significant biclusters in large expression data using a novel optimization technique called stellar-mass black hole optimization (SBO). This optimization algorithm is inspired from the property of the relentless pull of a black hole’s gravity that is present in the Universe. The proposed work is tested on benchmark optimization test functions and gene expression benchmark datasets, and the results are compared with swarm intelligence techniques such as particle swarm optimization (PSO), and cuckoo search (CK). The experimental results show that the SBO outperforms PSO and CK.