{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:01:27Z","timestamp":1775325687415,"version":"3.50.1"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2019,1,21]],"date-time":"2019-01-21T00:00:00Z","timestamp":1548028800000},"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":["61672184"],"award-info":[{"award-number":["61672184"]}],"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":["61822306"],"award-info":[{"award-number":["61822306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fok Ying-Tung Education Foundation for Young Teachers in the Higher Education Institutions of China","award":["161063"],"award-info":[{"award-number":["161063"]}]},{"name":"Scientific Research Foundation in Shenzhen","award":["JCYJ20150626110425228"],"award-info":[{"award-number":["JCYJ20150626110425228"]}]},{"name":"Scientific Research Foundation in Shenzhen","award":["JCYJ20170307152201596"],"award-info":[{"award-number":["JCYJ20170307152201596"]}]},{"name":"Guangdong Province High-Level Personnel of Special Support Program","award":["2016TX03X164"],"award-info":[{"award-number":["2016TX03X164"]}]},{"name":"Guangdong Province High-Level Personnel of Special Support Program","award":["2016TQ03X618"],"award-info":[{"award-number":["2016TQ03X618"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we proposed two algorithms: MV-fold and MT-fold. MV-fold is a new computational predictor based on the multi-view learning model for fold recognition. Different features of proteins were treated as different views of proteins, including the evolutionary information, secondary structure information and physicochemical properties. These different views constituted the latent space. The \u03b5-dragging technique was employed to enlarge the margins between different protein folds, improving the predictive performance of MV-fold. Then, MV-fold was combined with two template-based methods: HHblits and HMMER. The ensemble method is called MT-fold incorporating the advantages of both discriminative methods and template-based methods. Experimental results on five widely used benchmark datasets (DD, RDD, EDD, TG and LE) showed that the proposed methods outperformed some state-of-the-art methods in this field, indicating that MV-fold and MT-fold are useful computational tools for protein fold recognition and protein homology detection and would be efficient tools for protein sequence analysis. Finally, we constructed an update and rigorous benchmark dataset based on SCOPe (version 2.07) to fairly evaluate the performance of the proposed method, and our method achieved stable performance on this new dataset. This new benchmark dataset will become a widely used benchmark dataset to fairly evaluate the performance of different methods for fold recognition.<\/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\/btz040","type":"journal-article","created":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T12:18:49Z","timestamp":1547641129000},"page":"2982-2990","source":"Crossref","is-referenced-by-count":79,"title":["Protein fold recognition based on multi-view modeling"],"prefix":"10.1093","volume":"35","author":[{"given":"Ke","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen, Guangdong, China"}]},{"given":"Xiaozhao","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Guangdong University of Technology , Guangzhou, China"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen, Guangdong, China"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Shenzhen, Guangdong, China"},{"name":"School of Computer Science and Technology, Beijing Institute of Technology , Beijing, China"}]}],"member":"286","published-online":{"date-parts":[[2019,1,21]]},"reference":[{"key":"2023062711321946000_btz040-B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023062711321946000_btz040-B2","doi-asserted-by":"crossref","first-page":"i359","DOI":"10.1093\/bioinformatics\/btx266","article-title":"Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression","volume":"33","author":"Ammad-Ud-Din","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062711321946000_btz040-B3","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1093\/bioinformatics\/btx217","article-title":"EigenTHREADER: analogous protein fold recognition by efficient contact map threading","volume":"33","author":"Buchan","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062711321946000_btz040-B4","first-page":"596","author":"Cai","year":"2014"},{"key":"2023062711321946000_btz040-B5","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1093\/bioinformatics\/btx429","article-title":"ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank","volume":"33","author":"Chen","year":"2017","journal-title":"Bioinformatics"},{"key":"2023062711321946000_btz040-B6","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1093\/bib\/bbw108","article-title":"A comprehensive review and comparison of different computational methods for protein remote homology detection","volume":"9","author":"Chen","year":"2018","journal-title":"Brief. 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