Editorial: Machine learning for peptide structure, function, and design

dc.contributor.authorGe, Ruiquan
dc.contributor.authorDong, Chuan
dc.contributor.authorWang, Juexin
dc.contributor.authorWei, Yanjie
dc.contributor.departmentBiomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
dc.date.accessioned2024-10-24T11:20:11Z
dc.date.available2024-10-24T11:20:11Z
dc.date.issued2022-09-20
dc.eprint.versionFinal published version
dc.identifier.citationGe R, Dong C, Wang J, Wei Y. Editorial: Machine learning for peptide structure, function, and design. Front Genet. 2022;13:1007635. Published 2022 Sep 20. doi:10.3389/fgene.2022.1007635
dc.identifier.urihttps://hdl.handle.net/1805/44189
dc.language.isoen_US
dc.publisherFrontiers Media
dc.relation.isversionof10.3389/fgene.2022.1007635
dc.relation.journalFrontiers in Genetics
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcePMC
dc.subjectDeep learning
dc.subjectDrug design
dc.subjectFunctional peptides
dc.subjectMachine learning
dc.subjectPeptide therapeutics
dc.titleEditorial: Machine learning for peptide structure, function, and design
dc.typeArticle
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