Bowyer Sebastian, Allen David J, Furnham Nicholas
Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
Department of Comparative Biomedical Sciences, Section Infection and Immunity, School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
J Gen Virol. 2025 Jan;106(1). doi: 10.1099/jgv.0.002067.
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more. This review explores how machine learning has been applied to and has impacted the study of viruses, before addressing the strengths and limitations of its techniques and finally highlighting the next steps that are needed for the technology to reach its full potential in this challenging and ever-relevant research area.
RNA基因组病毒进化的复杂性和速度使得对具有流行或大流行潜力的变异体进行预测性识别具有挑战性。近年来，机器学习已成为应对这一挑战的能力日益强大的技术，因为方法和计算能力的进步显著提高了模型的性能，并导致其在各个行业和学科中广泛应用。机器学习技术在病毒研究中的新兴应用现已扩展，为处理大规模数据集提供了新工具，并导致了现有表型预测、系统发育分析、药物发现等工作流程的重塑。本综述探讨了机器学习如何应用于病毒研究并对其产生影响，然后讨论了其技术的优势和局限性，最后强调了该技术在这一具有挑战性且始终相关的研究领域充分发挥潜力所需的下一步措施。
