ReviewBMC bioinformatics2022
DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph.
Review in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- ELE: Estimating tissue-specific long noncoding RNA gene essentiality using graph neural networks.Quantitative biology (Beijing, China) · 2026Article
- Regulatory role of lncRNA DANCR in osteogenic differentiation of bone marrow mesenchymal stem cells through targeting the miR-19a-3p/PTEN/AKT/mTOR pathway.In vitro cellular & developmental biology. Animal · 2026Article
- Article
- Exploring necroptosis-associated genes: implications for immune responses and therapeutic strategies in diabetic foot ulcers.Archives of medical science : AMS · 2026Article
- Long noncoding RNAs and microRNAs in Endometriosis.Biomedicines · 2025Review
- Non-coding RNAs as a Critical Player in the Regulation of Inflammasome in Inflammatory Bowel Diseases; Emphasize on lncRNAs.Cell biochemistry and biophysics · 2025Review
- LncRNA-SNHG16 Protects Against Oxidative Stress-Induced Vascular Endothelial Cell Injury in Cardiovascular Diseases by Regulating the miR-23a-3p-GLS-Glutamine Metabolism Axis.Applied biochemistry and biotechnology · 2025Article
- Identification and coregulation pattern analysis of long noncoding RNAs in the mouse brain after Angiostrongylus cantonensis infection.Parasites & vectors · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.International journal of molecular sciences · 2023Review
- Biogenesis and function of exosome lncRNAs and their role in female pathological pregnancy.Frontiers in endocrinology · 2023Review
- A comprehensive overview of exosome lncRNAs: emerging biomarkers and potential therapeutics in endometriosis.Frontiers in endocrinology · 2023Review
- The role of long non-coding RNA ANRIL in the development of atherosclerosis.Non-coding RNA research · 2022Review
- Non-coding RNAs in skin cancers:Biological roles and molecular mechanisms.Frontiers in pharmacology · 2022Review
- SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network.Frontiers in oncology · 2022Article
- Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundLong non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20-24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable.
resultsIn our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%.
conclusionsThe experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning.
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Registered trials
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.