ArticleBMC bioinformatics2020
RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Metamorphosis and lncRNAs: A Close Relationship.Genesis (New York, N.Y. : 2000) · 2026Review
- FusionCLM: enhanced molecular property prediction via knowledge fusion of chemical language models.Journal of cheminformatics · 2025Article
- MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction.BMC genomics · 2025Article
- LncRNA-Protein Interactions: A Key to Deciphering LncRNA Mechanisms.Biomolecules · 2025Review
- Bioinformatics analysis of lncRNA and mRNA differentially expressed in patients with cervical cancer.Frontiers in bioinformatics · 2025Article
- Article
- Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.Frontiers in neurology · 2024Article
- Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.MicroRNA (Shariqah, United Arab Emirates) · 2024Article
- Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia.BMC medical informatics and decision making · 2023Article
- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.Frontiers in genetics · 2023Article
- SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes.Biology · 2022Article
- Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework.Frontiers in plant science · 2022Article
- Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.Frontiers in plant science · 2022Article
- SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information.Frontiers in genetics · 2022Article
- Staem5: A novel computational approachfor accurate prediction of m5C site.Molecular therapy. Nucleic acids · 2021Article
- Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.Briefings in bioinformatics · 2021Review
- In silico drug repositioning using deep learning and comprehensive similarity measures.BMC bioinformatics · 2021Article
- Recent advances on the machine learning methods in predicting ncRNA-protein interactions.Molecular genetics and genomics : MGG · 2021Review
- DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.BMC bioinformatics · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions.
resultsIn this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods.
conclusionsThe results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It's anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.
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