LBSA-DRIVER: A Novel Approach to Identifying Cancer Driver Genes Using List-Based Simulated Annealing

Abstract

Introduction: Cancer driver genes are genes responsible for cancer genesis; thus, identifying cancer-related genes is crucial in fostering cancer treatment. The accuracy in identifying cancer driver genes within the vast pool of normal passenger genes directly influences the efficacy of treatment approaches. Objective: This research aimed to effectively identify cancer driver genes using the List-based Simulated Annealing (LBSA) optimization technique. Method: The proposed model (LBSA-DRIVER) harnesses a list-based simulated annealing algorithm within a bipartite network to pinpoint cancer driver genes. The process begins with creating a bipartite graph that integrates gene mutations and expression data from carefully chosen datasets. The LBSA algorithm is then applied to the generated graph to identify and rank the genes, drawing insights from a biological interaction network. Result: Following the algorithm's development, rigorous experimental analyses have been conducted using four benchmark datasets from The Cancer Genome Atlas (TCGA) database. The datasets used were the Breast Cancer dataset (BRCA), Prostate Adenocarcinoma dataset (PRAD), Ovarian cancer dataset (OV), and Glioblastoma Multiforme dataset (GBM). Conclusion: Our findings, including precision, recall, F-score, and accuracy metrics, provide strong evidence of the effectiveness of the proposed model in identifying driver genes.