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Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.<\/jats:p>","DOI":"10.1093\/bib\/bbz081","type":"journal-article","created":{"date-parts":[[2019,6,11]],"date-time":"2019-06-11T11:08:34Z","timestamp":1560251314000},"page":"1437-1447","source":"Crossref","is-referenced-by-count":128,"title":["Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning"],"prefix":"10.1093","volume":"21","author":[{"given":"Jiajun","family":"Hong","sequence":"first","affiliation":[{"name":"Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China"},{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China"}]},{"given":"Yongchao","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China"},{"name":"School of Pharmaceutical Sciences, Chongqing University, Chongqing, China"}]},{"given":"Junbiao","family":"Ying","sequence":"additional","affiliation":[{"name":"College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China"}]},{"given":"Weiwei","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences, Chongqing University, Chongqing, China"}]},{"given":"Tian","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, 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