{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T12:13:38Z","timestamp":1771071218784,"version":"3.50.1"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T00:00:00Z","timestamp":1560470400000},"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772217"],"award-info":[{"award-number":["61772217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71771098"],"award-info":[{"award-number":["71771098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2016YXMS104"],"award-info":[{"award-number":["2016YXMS104"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2017KFYXJJ225"],"award-info":[{"award-number":["2017KFYXJJ225"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units\u2019 models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The method is available as DNN-Dom Server at http:\/\/isyslab.info\/DNN-Dom\/.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz464","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T19:18:49Z","timestamp":1559762329000},"page":"5128-5136","source":"Crossref","is-referenced-by-count":32,"title":["DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network"],"prefix":"10.1093","volume":"35","author":[{"given":"Qiang","family":"Shi","sequence":"first","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Weiya","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Siqi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Fanglin","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Yinghao","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]},{"given":"Zhidong","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology , Wuhan 430074, China"}]}],"member":"286","published-online":{"date-parts":[[2019,6,14]]},"reference":[{"key":"2023013108384880700_btz464-B1","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1093\/bioinformatics\/btx781","article-title":"DNCON2: improved protein contact prediction using two-level deep convolutional neural networks","volume":"34","author":"Adhikari","year":"2018","journal-title":"Bioinformatics"},{"key":"2023013108384880700_btz464-B2","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1093\/bioinformatics\/btg006","article-title":"PDP: protein domain parser","volume":"19","author":"Alexandrov","year":"2003","journal-title":"Bioinformatics"},{"key":"2023013108384880700_btz464-B3","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1093\/bioinformatics\/btx431","article-title":"DeepLoc: prediction of protein subcellular localization using deep learning","volume":"33","author":"Almagro Armenteros","year":"2017","journal-title":"Bioinformatics"},{"key":"2023013108384880700_btz464-B4","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1093\/nar\/gkn944","article-title":"FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator","volume":"37","author":"Bondugula","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023013108384880700_btz464-B5","doi-asserted-by":"crossref","first-page":"W158","DOI":"10.1093\/nar\/gkl331","article-title":"KemaDom: a web server for domain prediction using kernel machine with local context","volume":"34","author":"Chen","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023013108384880700_btz464-B6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10618-005-0023-5","article-title":"DOMpro: protein domain prediction using profiles, secondary structure, relative solvent accessibility, and recursive neural networks","volume":"13","author":"Cheng","year":"2006","journal-title":"Data Min. 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