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A boosting approach for prediction of protein-RNA binding residues

BACKGROUND: 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. RESULTS: We propo...

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Autores principales: Tang, Yongjun, Liu, Diwei, Wang, Zixiang, Wen, Ting, Deng, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773889/
https://www.ncbi.nlm.nih.gov/pubmed/29219069
http://dx.doi.org/10.1186/s12859-017-1879-2
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author Tang, Yongjun
Liu, Diwei
Wang, Zixiang
Wen, Ting
Deng, Lei
author_facet Tang, Yongjun
Liu, Diwei
Wang, Zixiang
Wen, Ting
Deng, Lei
author_sort Tang, Yongjun
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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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spelling pubmed-57738892018-01-26 A boosting approach for prediction of protein-RNA binding residues Tang, Yongjun Liu, Diwei Wang, Zixiang Wen, Ting Deng, Lei BMC Bioinformatics Research BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2017-12-01 /pmc/articles/PMC5773889/ /pubmed/29219069 http://dx.doi.org/10.1186/s12859-017-1879-2 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Tang, Yongjun
Liu, Diwei
Wang, Zixiang
Wen, Ting
Deng, Lei
A boosting approach for prediction of protein-RNA binding residues
title A boosting approach for prediction of protein-RNA binding residues
title_full A boosting approach for prediction of protein-RNA binding residues
title_fullStr A boosting approach for prediction of protein-RNA binding residues
title_full_unstemmed A boosting approach for prediction of protein-RNA binding residues
title_short A boosting approach for prediction of protein-RNA binding residues
title_sort boosting approach for prediction of protein-rna binding residues
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773889/
https://www.ncbi.nlm.nih.gov/pubmed/29219069
http://dx.doi.org/10.1186/s12859-017-1879-2
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