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

Junjie Chen | Zendy; Mingyue Guo | Zendy; Shumin Li | Zendy; Bin Liu | Zendy

Open Access

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

Author(s) -

Junjie Chen,

Mingyue Guo,

Shumin Li,

Bin Liu

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx429

Subject(s) - computer science , login , web server , sequence homology , homology (biology) , rank (graph theory) , web application , computational biology , data mining , bioinformatics , peptide sequence , world wide web , biology , the internet , operating system , genetics , mathematics , gene , combinatorics

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.

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