ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank

被引：39
作者：
Chen, Junjie [1 ]
Guo, Mingyue [1 ]
Li, Shumin [1 ]
Liu, Bin [1 ]
机构：
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen Grad Sch, Shenzhen 518055, Guangdong, Peoples R China
来源：
BIOINFORMATICS | 2017年 / 33卷 / 21期
基金：
中国国家自然科学基金;
关键词：
STRUCTURAL CLASSIFICATION; SEQUENCE; PREDICTION; DATABASE; SCOP;
D O I：
10.1093/bioinformatics/btx429
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection called ProtDec-LTR2.0 by combining ProtDec-Learning to Rank (LTR) and pseudo protein representation. Experimental results showed that the detection performance is obviously improved. The web server provides a user-friendly interface to explore the sequence and structure information of candidate proteins and find their conserved domains by launching a multiple sequence alignment tool.
引用
收藏
页码：3473 / 3476
页数：4
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