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A boosting approach for prediction of protein-RNA binding residues

Overview of attention for article published in BMC Bioinformatics, December 2017
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Title
A boosting approach for prediction of protein-RNA binding residues
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1879-2
Pubmed ID
Authors

Yongjun Tang, Diwei Liu, Zixiang Wang, Ting Wen, Lei Deng

Abstract

RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex. We propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties. In cross-validation experiments on the RBP170 data set show that PredRBR achieves an overall accuracy of 0.84, a sensitivity of 0.85, MCC of 0.55 and AUC of 0.92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost. We further calculate the feature importance of different feature categories and find that structural neighborhood characteristics are critical in the recognization of RNA binding residues. Also, PredRBR yields significantly better prediction accuracy on an independent test set (RBP101) in comparison with other state-of-the-art methods. The superior performance over existing RNA-binding residue prediction methods indicates the importance of the gradient tree boosting algorithm combined with the optimal selected features.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 34%
Researcher 3 10%
Student > Bachelor 2 7%
Student > Master 2 7%
Student > Doctoral Student 1 3%
Other 4 14%
Unknown 7 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 28%
Agricultural and Biological Sciences 4 14%
Computer Science 2 7%
Medicine and Dentistry 2 7%
Physics and Astronomy 1 3%
Other 2 7%
Unknown 10 34%
Attention Score in Context

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 01 December 2017.
All research outputs
#19,166,139
of 23,751,351 outputs
Outputs from BMC Bioinformatics
#6,478
of 7,432 outputs
Outputs of similar age
#330,262
of 441,526 outputs
Outputs of similar age from BMC Bioinformatics
#107
of 132 outputs
Altmetric has tracked 23,751,351 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,432 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.