Machine learning for phytopathology: from the molecular scale towards the network scale.
Journal:
Briefings in bioinformatics
PMID:
33787847
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.
Authors
Keywords
Bacterial Proteins
Bayes Theorem
Disease Resistance
Fungal Proteins
Gene Expression Regulation
Host-Pathogen Interactions
NLR Proteins
Pathogen-Associated Molecular Pattern Molecules
Plant Diseases
Plant Pathology
Plants
Protein Interaction Mapping
Protein Serine-Threonine Kinases
Receptors, Pattern Recognition
Support Vector Machine
Viral Proteins