ArticleBMC bioinformatics2017
A boosting approach for prediction of protein-RNA binding residues.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 41 citations in OpenAlex.
- RNA-protein interaction prediction without high-throughput data: An overview and benchmark ofComputational and structural biotechnology journal · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins.Nucleic acids research · 2023Article
- GTB-PPI: Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting.Genomics, proteomics & bioinformatics · 2020Article
- Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type.International journal of molecular sciences · 2020Review
- Article
- PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.Scientific reports · 2020Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
- MADOKA: an ultra-fast approach for large-scale protein structure similarity searching.BMC bioinformatics · 2019Article
- XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting.Genes · 2019Article
- Computational approaches for the analysis of RNA-protein interactions: A primer for biologists.The Journal of biological chemistry · 2019Review
- PredPRBA: Prediction of Protein-RNA Binding Affinity Using Gradient Boosted Regression Trees.Frontiers in genetics · 2019Article
- PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.BMC bioinformatics · 2018Article
- Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting.Scientific reports · 2018Article
- SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins.International journal of molecular sciences · 2018Article
- Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods.Frontiers in plant science · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRNA 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.
resultsWe 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.
conclusionsThe 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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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.