Machine learning for phytopathology: from the molecular scale towards the network scale. Issue 5 (31st March 2021)

• Record Type: Journal Article
• Authors: Wang, Yansu; Zhou, Murong; Zou, Quan; Xu, Lei
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-03-31
• Subjects: machine learning -- effectors -- R proteins -- plant–pathogen interaction network; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab037
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant–pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant–pathogen interactions and discuss the applications and advances of machine learning in plant–pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein–protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.