ArticleScientific reports2017
Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 90 citations in OpenAlex.
- NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions.Interdisciplinary sciences, computational life sciences · 2025Article
- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- The Idiopathic Pulmonary Fibrosis-Associated Single Nucleotide Polymorphism RS35705950 Is Transcribed in a MUC5B Promoter Associated Long Non-Coding RNA (AC061979.1).Non-coding RNA · 2022Article
- Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions.Interdisciplinary sciences, computational life sciences · 2022Article
- Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.Big data and cognitive computing · 2022Article
- 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
- A Survey of Current Resources to Study lncRNA-Protein Interactions.Non-coding RNA · 2021Review
- 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
- GBDTLRL2D Predicts LncRNA-Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network.Frontiers in cell and developmental biology · 2021Review
- Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes.Frontiers in genetics · 2021Article
- Role of taurine, its haloamines and its lncRNA TUG1 in both inflammation and cancer progression. On the road to therapeutics? (Review).International journal of oncology · 2020Review
- PRIME-3D2D is a 3D2D model to predict binding sites of protein-RNA interaction.Communications biology · 2020Article
- Article
- Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network.BioMed research international · 2020Article
- Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions.Computational and structural biotechnology journal · 2020Article
- The multifaceted roles of long noncoding RNAs in pancreatic cancer: an update on what we know.Cancer cell international · 2020Review
- LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions.Frontiers in genetics · 2020Article
- Computational identification of N6-methyladenosine sites in multiple tissues of mammals.Computational and structural biotechnology journal · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.
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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.