Ghosh Dibyendu, Chakraborty Srija, Kodamana Hariprasad, Chakraborty Supriya
Molecular Virology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016, India.
Virol J. 2022 Mar 9;19(1):42. doi: 10.1186/s12985-022-01767-5.
Inclusion of high throughput technologies in the field of biology has generated massive amounts of data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the task. Hence, researchers are turning to machine learning based approaches for the analysis of high-dimensional big data. In machine learning, once a model is trained with a training dataset, it can be applied on a testing dataset which is independent. In current times, deep learning algorithms further promote the application of machine learning in several field of biology including plant virology.
Plant viruses have emerged as one of the principal global threats to food security due to their devastating impact on crops and vegetables. The emergence of new viral strains and species help viruses to evade the concurrent preventive methods. According to a survey conducted in 2014, plant viruses are anticipated to cause a global yield loss of more than thirty billion USD per year. In order to design effective, durable and broad-spectrum management protocols, it is very important to understand the mechanistic details of viral pathogenesis. The application of machine learning enables precise diagnosis of plant viral diseases at an early stage. Furthermore, the development of several machine learning-guided bioinformatics platforms has primed plant virologists to understand the host-virus interplay better. In addition, machine learning has tremendous potential in deciphering the pattern of plant virus evolution and emergence as well as in developing viable control options.
Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning and comprehensively discusses the trends and prospects of machine learning in the diagnosis of viral diseases, understanding host-virus interplay and emergence of plant viruses.
近年来，生物学领域引入高通量技术产生了海量数据。如今，将这些海量数据转化为知识是计算生物学面临的主要挑战。传统的数据分析方法已无法完成这项任务。因此，研究人员正转向基于机器学习的方法来分析高维大数据。在机器学习中，一旦使用训练数据集训练了一个模型，它就可以应用于独立的测试数据集。当前，深度学习算法进一步推动了机器学习在包括植物病毒学在内的多个生物学领域的应用。
植物病毒因其对农作物和蔬菜的毁灭性影响，已成为全球粮食安全的主要威胁之一。新病毒株和病毒种类的出现帮助病毒规避了现有的预防方法。根据2014年进行的一项调查，预计植物病毒每年将造成全球超过300亿美元的产量损失。为了设计有效、持久和广谱的管理方案，了解病毒发病机制的详细机理非常重要。机器学习的应用能够在早期精确诊断植物病毒病。此外，几个机器学习指导的生物信息学平台的开发使植物病毒学家能够更好地理解宿主与病毒的相互作用。此外，机器学习在解读植物病毒进化和出现的模式以及开发可行的控制方法方面具有巨大潜力。
鉴于机器学习在理解植物病毒学方面的应用取得了重大进展，本综述重点介绍了机器学习的入门知识，并全面讨论了机器学习在病毒病诊断、理解宿主与病毒相互作用以及植物病毒出现方面的趋势和前景。
