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

TL;DR

This paper reviews how machine learning, especially deep learning, is transforming plant virus research by improving diagnosis, understanding host-virus interactions, and tracking virus emergence, addressing the challenge of analyzing large biological datasets.

Contribution

It provides a comprehensive overview of current trends and future prospects of machine learning applications in plant virology, highlighting recent advances and potential directions.

Findings

Machine learning enhances viral disease diagnosis.

Deep learning aids in understanding host-virus interactions.

ML approaches help track emergence of plant viruses.

Abstract

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.