ArticleBMC bioinformatics2017
Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions.
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 24 papers.
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24 citing papers in PubMed.
- MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction.BMC genomics · 2025Article
- Fusion of multi-source relationships and topology to infer lncRNA-protein interactions.Molecular therapy. Nucleic acids · 2024Article
- LncRNA-protein interaction prediction with reweighted feature selection.BMC bioinformatics · 2023Article
- Artificial intelligence methods enhance the discovery of RNA interactions.Frontiers in molecular biosciences · 2022Review
- SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information.Frontiers in genetics · 2022Article
- LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification.BioData mining · 2021Article
- LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification.BMC bioinformatics · 2021Article
- LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification.BMC bioinformatics · 2021Article
- A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.Scientific reports · 2021Article
- Recent advances on the machine learning methods in predicting ncRNA-protein interactions.Molecular genetics and genomics : MGG · 2021Review
- PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction.Communications biology · 2020Article
- Heterogeneous graph inference based on similarity network fusion for predicting lncRNA-miRNA interaction.RSC advances · 2020Article
- Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection.Frontiers in bioengineering and biotechnology · 2020Article
- LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination.BMC genomics · 2019Article
- Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods.International journal of molecular sciences · 2019Article
- In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.Molecular therapy. Nucleic acids · 2019Article
- Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.Frontiers in genetics · 2019Review
- Predicting lncRNA-Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model.Frontiers in genetics · 2019Article
- Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction.Frontiers in genetics · 2019Article
- LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm.Frontiers in genetics · 2019Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLong non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs.
resultsIn this paper, we propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). Concretely, we first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods.
conclusionFusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs.
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