Optimization of Gene Selection for Cancer Classification in High-Dimensional Data Using an Improved African Vultures Algorithm

• Published in: Algorithms Vol. 17; no. 8; p. 342
• Main Authors: Gafar, Mona G., Abohany, Amr A., Elkhouli, Ahmed E., El-Mageed, Amr A. Abd
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; African vultures algorithm; Algorithms; Biomarkers; Cancer; Classification; Datasets; Deoxyribonucleic acid; differential evolution; Dimensional analysis; DNA; Evolutionary computation; Feature selection; Gene expression; gene selection; Gene sequencing; Genes; Genetic aspects; Genetic markers; Genetic transcription; Heuristic; Heuristic methods; high dimensionality; Investigations; Mathematical optimization; meta-heuristic algorithms; Methods; Optimization; Random variables; Ribonucleic acid; RNA; RNA sequencing; Support vector machines; Saudi Arabia
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a17080342

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon’s rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers.