A Hybrid Ant Colony Optimization Algorithm for Human Monkeypox DNA Codon Selection

Abstract

Effective codon selection is a pivotal aspect of heterologous gene expression, significantly impact on protein synthesis. Common strategies often rely on the prevalent usage of host genome codons, but concerns persist about their reliability. The substantial asymmetry between gene dimensionality and sample size can result in inaccuracies in disease diagnosis within clinical settings. This paper introduces a modified hybrid ant colony optimization and the support vector machine (ACO-SVM) algorithm, utilize as a classifier on the extracted codon in the monkeypox virus DNA sequence. Experimental outcomes on monkeypox virus DNA datasets reveal that the proposed approach outperforms in recognizing monkeypox virus codon selection. This underscores the efficacy of the modified ACO as a valuable tool for codon selection in the monkeypox virus and the extraction of meaningful information from high-dimensional data.  In the context of vaccines design, optimized codons in a viral vector escalate the production of viral antigens, fostering a more potent and effective immune response and ultimately enhancing vaccine efficacy. This research ensures that viral agents are meticulously tuned for optimal efficiency and adaptability across diverse applications, ranging from gene therapy to vaccine development.