{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:43:10Z","timestamp":1753875790121,"version":"3.41.2"},"reference-count":136,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM089753"],"award-info":[{"award-number":["R01GM089753"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61832019"],"award-info":[{"award-number":["61832019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"publisher","award":["OGP0046506"],"award-info":[{"award-number":["OGP0046506"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In this article, we review two challenging computational questions in protein science: neoantigen prediction and protein structure prediction. Both topics have seen significant leaps forward by deep learning within the past five years, which immediately unlocked new developments of drugs and immunotherapies. We show that deep learning models offer unique advantages, such as representation learning and multi-layer architecture, which make them an ideal choice to leverage a huge amount of protein sequence and structure data to address those two problems. We also discuss the impact and future possibilities enabled by those two applications, especially how the data-driven approach by deep learning shall accelerate the progress towards personalized biomedicine.<\/jats:p>","DOI":"10.1093\/bib\/bbab493","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T19:10:49Z","timestamp":1635534649000},"source":"Crossref","is-referenced-by-count":12,"title":["A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5186-8852","authenticated-orcid":false,"given":"Ngoc Hieu","family":"Tran","sequence":"first","affiliation":[{"name":"University of Waterloo, Canada"}]},{"given":"Jinbo","family":"Xu","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute at Chicago, USA"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"University of Waterloo, Canada"}]}],"member":"286","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"2022012000311130100_ref1","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1038\/s41592-021-01163-x","article-title":"A celebration of structural biology","volume":"18","year":"2021","journal-title":"Nat Methods"},{"key":"2022012000311130100_ref2","doi-asserted-by":"crossref","first-page":"D1100","DOI":"10.1093\/nar\/gkw936","article-title":"The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition","volume":"45","author":"Deutsch","year":"2017","journal-title":"Nucleic Acids 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