{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:09:56Z","timestamp":1780355396542,"version":"3.54.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"21","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Protein remote homology detection is one of the fundamental problems in computational biology, aiming to find protein sequences in a database of known structures that are evolutionarily related to a given query protein. Some computational methods treat this problem as a ranking problem and achieve the state-of-the-art performance, such as PSI-BLAST, HHblits and ProtEmbed. This raises the possibility to combine these methods to improve the predictive performance. In this regard, we are to propose a new computational method called ProtDec-LTR for protein remote homology detection, which is able to combine various ranking methods in a supervised manner via using the Learning to Rank (LTR) algorithm derived from natural language processing.<\/jats:p>\n               <jats:p>Results: Experimental results on a widely used benchmark dataset showed that ProtDec-LTR can achieve an ROC1 score of 0.8442 and an ROC50 score of 0.9023 outperforming all the individual predictors and some state-of-the-art methods. These results indicate that it is correct to treat protein remote homology detection as a ranking problem, and predictive performance improvement can be achieved by combining different ranking approaches in a supervised manner via using LTR.<\/jats:p>\n               <jats:p>Availability and implementation: For users\u2019 convenience, the software tools of three basic ranking predictors and Learning to Rank algorithm were provided at http:\/\/bioinformatics.hitsz.edu.cn\/ProtDec-LTR\/home\/<\/jats:p>\n               <jats:p>Contact: \u00a0bliu@insun.hit.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv413","type":"journal-article","created":{"date-parts":[[2015,7,12]],"date-time":"2015-07-12T00:20:01Z","timestamp":1436660401000},"page":"3492-3498","source":"Crossref","is-referenced-by-count":83,"title":["Application of learning to rank to protein remote homology detection"],"prefix":"10.1093","volume":"31","author":[{"given":"Bin","family":"Liu","sequence":"first","affiliation":[{"name":"1 School of Computer Science and Technology,"},{"name":"2 Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China and"},{"name":"3 Gordon Life Science Institute, Belmont, MA 02478, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Chen","sequence":"additional","affiliation":[{"name":"1 School of Computer Science and Technology,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[{"name":"1 School of Computer Science and Technology,"},{"name":"2 Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China and"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,7,10]]},"reference":[{"key":"2023020202324551800_btv413-B1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0022-2836(05)80360-2","article-title":"Basic local alignment search tool","volume":"215","author":"Altschul","year":"1990","journal-title":"J. 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