{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T21:40:31Z","timestamp":1764020431929,"version":"3.41.2"},"reference-count":128,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Research Funding of Shenzhen Polytechnic","award":["6020320002K"],"award-info":[{"award-number":["6020320002K"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant\u2013pathogen 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\u2013pathogen interactions and discuss the applications and advances of machine learning in plant\u2013pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein\u2013protein 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.<\/jats:p>","DOI":"10.1093\/bib\/bbab037","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T13:55:33Z","timestamp":1611842133000},"source":"Crossref","is-referenced-by-count":10,"title":["Machine learning for phytopathology: from the molecular scale towards the network scale"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1274-4958","authenticated-orcid":false,"given":"Yansu","family":"Wang","sequence":"first","affiliation":[{"name":"Postdoctoral Innovation Practice Base, Shenzhen Polytechnic, China"}]},{"given":"Murong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shenzhen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Shenzhen Polytechnic, China"}]}],"member":"286","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"2021090815135373000_ref1","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1093\/bib\/bbk007","article-title":"Machine learning in bioinformatics","volume":"7","author":"Larranaga","year":"2006","journal-title":"Brief Bioinform"},{"key":"2021090815135373000_ref2","doi-asserted-by":"crossref","first-page":"e116","DOI":"10.1371\/journal.pcbi.0030116","article-title":"Machine learning and its applications to biology","volume":"3","author":"Tarca","year":"2007","journal-title":"PLoS Comput Biol"},{"volume-title":"Bioinformatics: the machine learning 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