{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:27:33Z","timestamp":1774592853932,"version":"3.50.1"},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"European Research Council","doi-asserted-by":"publisher","award":["695558"],"award-info":[{"award-number":["695558"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["FC001002"],"award-info":[{"award-number":["FC001002"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"crossref","award":["FC001002"],"award-info":[{"award-number":["FC001002"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["FC001002"],"award-info":[{"award-number":["FC001002"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Over the past 50\u2009years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of proteins, single orphan sequences, remains unsolved.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>By taking a bioinformatics approach to semi-supervised machine learning, we develop Profile Augmentation of Single Sequences (PASS), a simple but powerful framework for building accurate single-sequence methods. To demonstrate the effectiveness of PASS we apply it to the mature field of secondary structure prediction. In doing so we develop S4PRED, the successor to the open-source PSIPRED-Single method, which achieves an unprecedented Q3 score of 75.3% on the standard CB513 test. PASS provides a blueprint for the development of a new generation of predictive methods, advancing our ability to model individual protein sequences.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The S4PRED model is available as open source software on the PSIPRED GitHub repository (https:\/\/github.com\/psipred\/s4pred), along with documentation. It will also be provided as a part of the PSIPRED web service (http:\/\/bioinf.cs.ucl.ac.uk\/psipred\/).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab491","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T19:22:52Z","timestamp":1625080972000},"page":"3744-3751","source":"Crossref","is-referenced-by-count":67,"title":["Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9378-1250","authenticated-orcid":false,"given":"Lewis","family":"Moffat","sequence":"first","affiliation":[{"name":"Department of Computer Science, University College London , London WC1E 6BT, UK"},{"name":"Biomedical Data Science Laboratory, The Francis Crick Institute , London NW1 1AT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8626-3765","authenticated-orcid":false,"given":"David T","family":"Jones","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University College London , London WC1E 6BT, UK"},{"name":"Biomedical Data Science Laboratory, The Francis Crick Institute , London NW1 1AT, UK"}]}],"member":"286","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"2023051608254732300_btab491-B1","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat. 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