ArticleFrontiers in genetics2018
IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed.
- Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.BMC genomics · 2024Article
- Hyperbolic matrix factorization improves prediction of drug-target associations.Scientific reports · 2023Article
- 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
- Long Non-Coding RNAs: The New Frontier into Understanding the Etiology of Alcohol Use Disorder.Non-coding RNA · 2022Review
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification.BioData mining · 2021Article
- A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases.Interdisciplinary sciences, computational life sciences · 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
- Using Network Distance Analysis to Predict lncRNA-miRNA Interactions.Interdisciplinary sciences, computational life sciences · 2021Article
- A Survey of Current Resources to Study lncRNA-Protein Interactions.Non-coding RNA · 2021Review
- Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network.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
- Comprehensive analysis of lncRNA biomarkers in kidney renal clear cell carcinoma by lncRNA-mediated ceRNA network.PloS one · 2021Article
- Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization.BioMed research international · 2021Article
- Roles of Emerging RNA-Binding Activity of cGAS in Innate Antiviral Response.Frontiers in immunology · 2021Review
- Predicting lncRNA-Protein Interaction With Weighted Graph-Regularized Matrix Factorization.Frontiers in genetics · 2021Article
- Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma.PloS one · 2021Article
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6 authors.
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Abstract
Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the "Mus musculus," many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.
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