期刊
BIOINFORMATICS
卷 31, 期 21, 页码 3492-3498出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv413
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资金
- National Natural Science Foundation of China [61300112, 61272383]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars
- State Education Ministry
- Natural Science Foundation of Guangdong Province [2014A030313695]
- Strategic Emerging Industry Development Special Funds of Shenzhen [JCYJ20140508161040764]
- Shenzhen Municipal Science and Technology Innovation Council [CXZZ20140904154910774]
- National High Technology Research and Development Program of China (863 Program) [2015AA015405]
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. 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.
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