Application of learning to rank to protein remote homology detection

Bin Liu*, Junjie Chen, Xiaolong Wang

*Corresponding author for this work

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Abstract

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. Availability and implementation: For users' 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/.

Original languageEnglish
Pages (from-to)3492-3498
Number of pages7
JournalBioinformatics
Volume31
Issue number21
DOIs
Publication statusPublished - 18 Dec 2015
Externally publishedYes

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Liu, B., Chen, J., & Wang, X. (2015). Application of learning to rank to protein remote homology detection. Bioinformatics, 31(21), 3492-3498. https://doi.org/10.1093/bioinformatics/btv413