ArticleJournal of cellular and molecular medicine2020
LNRLMI: Linear neighbour representation for predicting lncRNA-miRNA interactions.
Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Pivotal Role of miRNA-lncRNA Interactions in Human Diseases.Molecular biotechnology · 2025Review
- A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions.Computational and structural biotechnology journal · 2024Article
- GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm.BMC bioinformatics · 2023Article
- BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA-miRNA interaction prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph.BMC bioinformatics · 2022Review
- LINC00961 functions as an anti-oncogene in non-small cell lung carcinoma by regulation of miR-3127.American journal of translational research · 2022Article
- LncRNA-AC009948.5 promotes invasion and metastasis of lung adenocarcinoma by binding to miR-186-5p.Frontiers in oncology · 2022Article
- SPCMLMI: A structural perturbation-based matrix completion method to predict lncRNA-miRNA interactions.Frontiers in genetics · 2022Article
- Functional Interaction among lncRNA HOTAIR and MicroRNAs in Cancer and Other Human Diseases.Cancers · 2021Review
- Imbalance of Molecular Module of TINCR-miR-761 Promotes the Metastatic Potential of Early Triple Negative Breast Cancer and Partially Offsets the Anti-Tumor Activity of Luteolin.Cancer management and research · 2021Article
- WAFNRLTG: A Novel Model for Predicting LncRNA Target Genes Based on Weighted Average Fusion Network Representation Learning Method.Frontiers in cell and developmental biology · 2021Article
- Construction of ceRNA network to identify the lncRNA and mRNA related to non-small cell lung cancer.PloS one · 2021Article
- MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network.ACS omega · 2020Article
- Heterogeneous graph inference based on similarity network fusion for predicting lncRNA-miRNA interaction.RSC advances · 2020Article
- LNRLMI: Linear neighbour representation for predicting lncRNA-miRNA interactions.Journal of cellular and molecular medicine · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA-miRNA interactions from CLIP-seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA-miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA-miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k-fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2-fold, 5-fold and 10-fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA-miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non-coding RNA regulation network that lncRNA and miRNA are involved in.
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