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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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Mentioned by

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3 tweeters

Citations

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34 Dimensions

Readers on

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25 Mendeley
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1 CiteULike
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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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 36%
Researcher 3 12%
Student > Master 2 8%
Student > Bachelor 2 8%
Lecturer 1 4%
Other 3 12%
Unknown 5 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 28%
Agricultural and Biological Sciences 3 12%
Computer Science 2 8%
Medicine and Dentistry 2 8%
Physics and Astronomy 1 4%
Other 2 8%
Unknown 8 32%

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 02 December 2017.
All research outputs
#7,734,533
of 12,378,936 outputs
Outputs from BMC Bioinformatics
#3,160
of 4,543 outputs
Outputs of similar age
#195,759
of 353,140 outputs
Outputs of similar age from BMC Bioinformatics
#112
of 187 outputs
Altmetric has tracked 12,378,936 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,543 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,140 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 187 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.