Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization

Abstract

Microarray technology, as applied to the fields of bioinformatics, biotechnology, and bioengineering, has made remarkable progress in both the treatment and prediction of many biological problems. However, this technology presents a critical challenge due to the size of the numerous genes present in the high-dimensional biological datasets associated with an experiment, which leads to a curse of dimensionality on biological data. Such high dimensionality of real biological data sets not only increases memory requirements and training costs, but also reduces the ability of learning algorithms to generalise. Consequently, multiple feature selection (FS) methods have been proposed by researchers to choose the most significant and precise subset of classified genes from gene expression datasets while maintaining high classification accuracy. In this research work, a novel binary method called iBABC-CGO based on the island model of the artificial bee colony algorithm, combined with the chaos game optimization algorithm and SVM classifier, is suggested for FS problems using gene expression data. Due to the binary nature of FS problems, two distinct transfer functions are employed for converting the continuous search space into a binary one, thus improving the efficiency of the exploration and exploitation phases. The suggested strategy is tested on a variety of biological datasets with different scales and compared to popular metaheuristic-based, filter-based, and hybrid FS methods. Experimental results supplemented with the statistical measures, box plots, Wilcoxon tests, Friedman tests, and radar plots demonstrate that compared to prior methods, the proposed iBABC-CGO exhibit competitive performance in terms of classification accuracy, selection of the most relevant subset of genes, data variability, and convergence rate. The suggested method is also proven to identify unique sets of informative, relevant genes successfully with the highest overall average accuracy in 15 tested biological datasets. Additionally, the biological interpretations of the selected genes by the proposed method are also provided in our research work.