Evidence map›Paper›PMID 27581095›Full record

ArticleScientific reports2016

dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation.

Junjie Chen, Ren Long, Xiao-Long Wang, Bin Liu, Kuo-Chen Chou

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Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

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25citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  9. A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019
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  10. RAFTSBMC bioinformatics · 2019
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Junjie ChenSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
Ren LongSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
Xiao-Long WangSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
Bin LiuSchool of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
Kuo-Chen ChouGordon Life Science Institute, Boston, MA 02478, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein remote homology detection is an important task in computational proteomics. Some computational methods have been proposed, which detect remote homology proteins based on different features and algorithms. As noted in previous studies, their predictive results are complementary to each other. Therefore, it is intriguing to explore whether these methods can be combined into one package so as to further enhance the performance power and application convenience. In view of this, we introduced a protein representation called profile-based pseudo protein sequence to extract the evolutionary information from the relevant profiles. Based on the concept of pseudo proteins, a new predictor, called "dRHP-PseRA", was developed by combining four state-of-the-art predictors (PSI-BLAST, HHblits, Hmmer, and Coma) via the rank aggregation approach. Cross-validation tests on a SCOP benchmark dataset have demonstrated that the new predictor has remarkably outperformed any of the existing methods for the same purpose on ROC50 scores. Accordingly, it is anticipated that dRHP-PseRA holds very high potential to become a useful high throughput tool for detecting remote homology proteins. For the convenience of most experimental scientists, a web-server for dRHP-PseRA has been established at http://bioinformatics.hitsz.edu.cn/dRHP-PseRA/.

Indexed as

AlgorithmsSequence Homology, Amino AcidAmino Acid SequenceDatabases, ProteinSequence Analysis, Protein

Identifiers

PMID27581095
PMCPMC5007510

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.