  Inclusion of high throughput technologies in the field of biology has
generated massive amounts of biological 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. 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 diagnosis of viral diseases, understanding
host-virus interplay and emergence of plant viruses.
