ArticleScientific reports2018
A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- Detecting protein higher-order structural changes using kinase as a phospho-labeler.Cell reports methods · 2026Article
- RNA-protein interaction prediction without high-throughput data: An overview and benchmark ofComputational and structural biotechnology journal · 2024Article
- VirVACPRED: A Web Server for Prediction of Protective Viral Antigens.International journal of peptide research and therapeutics · 2022Article
- A deep learning model for plant lncRNA-protein interaction prediction with graph attention.Molecular genetics and genomics : MGG · 2020Article
- The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility.Frontiers in genetics · 2020Article
- Article
- Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.The journal of physical chemistry. B · 2019Article
- Identification of miR-200c and miR141-Mediated lncRNA-mRNA Crosstalks in Muscle-Invasive Bladder Cancer Subtypes.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.
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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.