Editorial: Machine learning for peptide structure, function, and design
dc.contributor.author | Ge, Ruiquan | |
dc.contributor.author | Dong, Chuan | |
dc.contributor.author | Wang, Juexin | |
dc.contributor.author | Wei, Yanjie | |
dc.contributor.department | Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering | |
dc.date.accessioned | 2024-10-24T11:20:11Z | |
dc.date.available | 2024-10-24T11:20:11Z | |
dc.date.issued | 2022-09-20 | |
dc.eprint.version | Final published version | |
dc.identifier.citation | Ge 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.uri | https://hdl.handle.net/1805/44189 | |
dc.language.iso | en_US | |
dc.publisher | Frontiers Media | |
dc.relation.isversionof | 10.3389/fgene.2022.1007635 | |
dc.relation.journal | Frontiers in Genetics | |
dc.rights | Attribution 4.0 International | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | PMC | |
dc.subject | Deep learning | |
dc.subject | Drug design | |
dc.subject | Functional peptides | |
dc.subject | Machine learning | |
dc.subject | Peptide therapeutics | |
dc.title | Editorial: Machine learning for peptide structure, function, and design | |
dc.type | Article |