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Inclusion of high throughput technologies in the field of biology hasgenerated massive amounts of biological data in the recent years. Now,transforming these huge volumes of data into knowledge is the primary challengein computational biology. The traditional methods of data analysis have failedto carry out the task. Hence, researchers are turning to machine learning basedapproaches 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 testingdataset which is independent. In current times, deep learning algorithmsfurther promote the application of machine learning in several field of biologyincluding plant virology. Considering a significant progress in the applicationof machine learning in understanding plant virology, this review highlights anintroductory note on machine learning and comprehensively discusses the trendsand prospects of machine learning in diagnosis of viral diseases, understandinghost-virus interplay and emergence of plant viruses.

Application of Machine Learning in understanding plant virus  pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus  interplay and management