{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:59:51Z","timestamp":1770645591589,"version":"3.49.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JCQY202108"],"award-info":[{"award-number":["JCQY202108"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Startup Foundation for Advanced Talents at Nanjing Agricultural University","award":["050\/804009"],"award-info":[{"award-number":["050\/804009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. However, the existing experimental techniques for detecting Kcr sites are cost-ineffective, to a great need for new computational methods to address this problem. We here describe Adapt-Kcr, an advanced deep learning model that utilizes adaptive embedding and is based on a convolutional neural network together with a bidirectional long short-term memory network and attention architecture. On the independent testing set, Adapt-Kcr outperformed the current state-of-the-art Kcr prediction model, with an improvement of 3.2% in accuracy and 1.9% in the area under the receiver operating characteristic curve. Compared to other Kcr models, Adapt-Kcr additionally had a more robust ability to distinguish between crotonylation and other lysine modifications. Another model (Adapt-ST) was trained to predict phosphorylation sites in SARS-CoV-2, and outperformed the equivalent state-of-the-art phosphorylation site prediction model. These results indicate that self-adaptive embedding features perform better than handcrafted features in capturing discriminative information; when used in attention architecture, this could be an effective way of identifying protein Kcr sites. Together, our Adapt framework (including learning embedding features and attention architecture) has a strong potential for prediction of other protein posttranslational modification sites.<\/jats:p>","DOI":"10.1093\/bib\/bbac037","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T12:10:29Z","timestamp":1643199029000},"source":"Crossref","is-referenced-by-count":39,"title":["Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture"],"prefix":"10.1093","volume":"23","author":[{"given":"Zutan","family":"Li","sequence":"first","affiliation":[{"name":"College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China"}]},{"given":"Jingya","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China"}]},{"given":"Shining","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Science, Nanjing Agricultural University, China"}]},{"given":"Liangyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Science, Nanjing Agricultural University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7953-1845","authenticated-orcid":false,"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Science, Nanjing Agricultural University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-2926","authenticated-orcid":false,"given":"Cong","family":"Pian","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Science, Nanjing Agricultural University, China"},{"name":"The State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing, 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