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PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles

Overview of attention for article published in Amino Acids, January 2008
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Citations

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Readers on

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28 Mendeley
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2 CiteULike
Title
PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles
Published in
Amino Acids, January 2008
DOI 10.1007/s00726-007-0634-9
Pubmed ID
Authors

Y. Wang, Z. Xue, G. Shen, J. Xu

Abstract

Protein-RNA interactions play a key role in a number of biological processes such as protein synthesis, mRNA processing, assembly and function of ribosomes and eukaryotic spliceosomes. A reliable identification of RNA-binding sites in RNA-binding proteins is important for functional annotation and site-directed mutagenesis. We developed a novel method for the prediction of protein residues that interact with RNA using support vector machine (SVM) and position-specific scoring matrices (PSSMs). Two cases have been considered in the prediction of protein residues at RNA-binding surfaces. One is given the sequence information of a protein chain that is known to interact with RNA; the other is given the structural information. Thus, five different inputs have been tested. Coupled with PSI-BLAST profiles and predicted secondary structure, the present approach yields a Matthews correlation coefficient (MCC) of 0.432 by a 7-fold cross-validation, which is the best among all previous reported RNA-binding sites prediction methods. When given the structural information, we have obtained the MCC value of 0.457, with PSSMs, observed secondary structure and solvent accessibility information assigned by DSSP as input. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/printr/ .

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
United Kingdom 1 4%
China 1 4%
Canada 1 4%
Unknown 24 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 39%
Student > Ph. D. Student 7 25%
Student > Master 3 11%
Student > Bachelor 3 11%
Professor 2 7%
Other 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 64%
Biochemistry, Genetics and Molecular Biology 4 14%
Computer Science 4 14%
Engineering 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 May 2014.
All research outputs
#10,837,563
of 12,226,226 outputs
Outputs from Amino Acids
#851
of 1,015 outputs
Outputs of similar age
#164,263
of 198,537 outputs
Outputs of similar age from Amino Acids
#17
of 21 outputs
Altmetric has tracked 12,226,226 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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