ArticleNucleic acids research2015
RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
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Who cites it
103 citing papers in PubMed.
- A Deep Learning Framework for Accurate lncRNA-Protein Interaction Prediction via Multi-Kernel Graph Embeddings and Self-Attention.Interdisciplinary sciences, computational life sciences · 2026Article
- Reliable evaluation and learning in multi-input biological association prediction.Briefings in bioinformatics · 2026Article
- Review
- NPI-HetGNN: A Prediction Model of ncRNA-Protein Interactions Based on Heterogeneous Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions.Interdisciplinary sciences, computational life sciences · 2025Article
- LncPTPred: predicting lncRNA-protein interaction based on crosslinking and immunoprecipitation (CLIP-Seq) data.Briefings in bioinformatics · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Graph-RPI: predicting RNA-protein interactions via graph autoencoder and self-supervised learning strategies.Briefings in bioinformatics · 2025Article
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- Inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction prediction in heterogeneous graphs.Briefings in bioinformatics · 2025Article
- A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only.Bioinformatics and biology insights · 2025Article
- Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer.Computational and structural biotechnology journal · 2025Article
- CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning.Scientific reports · 2024Article
- A pan-cancer interrogation of intronic polyadenylation and its association with cancer characteristics.Briefings in bioinformatics · 2024Article
- LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.Interdisciplinary sciences, computational life sciences · 2024Article
- Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.MicroRNA (Shariqah, United Arab Emirates) · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.Nucleic acids research · 2023Article
43 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.
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Registered trials
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.