Quantum squirrel inspired algorithm for gene selection in methylation and expression data of prostate cancer

Prostate cancer is the second most common type of cancer among men after skin cancer In this work, we present a comprehensive view on genomic and epigenomic changes following the incremental biological functionality. For gene selection, a new Feature Selection algorithm called Quantum Squirrel inspired Feature Selection is proposed here. While exploring the feature space, the proposed algorithm exploits the benefits of Squirrel Search Algorithm (a recently proposed swarm intelligence algorithm) along with Quantum mechanics. Moreover, a modified version of the end of winter concept is used to achieve effective dimension reduction capacity. Quantum Squirrel inspired Feature Selection is executed on both expression and methylation data of prostate cancer. The major challenge in gene selection is to bring down the number of selected features without compromising on accuracy. The proposed algorithm consistently achieves this goal and outperforms other state-of-the-art algorithms. The proposed algorithm has steadily attained 100% accuracy while selecting a much lower number of features (around 4), which is a major improvement over others. The top selected genes are biologically validated in terms of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontologies (GO), which further demonstrates the usefulness of the proposed method. The genes selected by Quantum Squirrel inspired Feature Selection show an association with prostate carcinoma and most are known biomarkers. A few novel biomarkers selected by proposed algorithm have also been detailed in this work. (C) 2021 Elsevier B.V. All rights reserved.