{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T11:47:55Z","timestamp":1768477675576,"version":"3.49.0"},"reference-count":274,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"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":["2017KFYXJJ225"],"award-info":[{"award-number":["2017KFYXJJ225"]}],"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":["2016YXMS104"],"award-info":[{"award-number":["2016YXMS104"]}],"id":[{"id":"10.13039\/501100012226","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\/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"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbz156","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T14:19:12Z","timestamp":1574086752000},"page":"194-218","source":"Crossref","is-referenced-by-count":57,"title":["Deep learning for mining protein data"],"prefix":"10.1093","volume":"22","author":[{"given":"Qiang","family":"Shi","sequence":"first","affiliation":[{"name":"School of Software Engineering, Huazhong University of Science and Technology. His main interests cover machine learning especially deep learning, protein data analysis, and big data mining"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiya","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, virtual reality, and data visualization"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Huang","sequence":"additional","affiliation":[{"name":"Software Engineering at Huazhong University of science and technology, focusing on Machine learning and data mining"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of life, University of Science & Technology; her main interests cover protein structure and function prediction and big data mining"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3559-0867","authenticated-orcid":false,"given":"Zhidong","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. 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