ArticleMolecular therapy. Nucleic acids2020
Discovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines.
Article in Molecular therapy. Nucleic acids, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multi-granularity transformer contrastive learning and feature reconstruction for prediction of disease-related miRNAs.BMC bioinformatics · 2026Article
- Machine Learning-Based Ensemble Feature Selection and Nested Cross-Validation for miRNA Biomarker Discovery in Usher Syndrome.Bioengineering (Basel, Switzerland) · 2025Article
- Identification of circulating miRNA as early diagnostic molecular markers in malignant glioblastoma base on decision tree joint scoring algorithm.Journal of cancer research and clinical oncology · 2023Article
- A novel cross-species differential tumor classification method based on exosome-derived microRNA biomarkers established by human-dog lymphoid and mammary tumor cell lines' transcription profiles.Veterinary world · 2022Article
- Inhibitory Effect of miR-339-5p on Glioma through PTP4A1/HMGB1 Pathway.Disease markers · 2022Article
- Mechanism of Exosomal LncRNA PART1 in Esophageal Cancer Angiogenesis by Targeting miR-302a-3p/CDC25A Axis.Technology in cancer research & treatmentArticle
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7 authors.
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Abstract
MicroRNAs (miRNAs) have been shown to be closely related to cancer progression. Traditional methods for discovering cancer-related miRNAs mostly require significant marginal differential expression, but some cancer-related miRNAs may be non-differentially or only weakly differentially expressed. Such miRNAs are called dark matters miRNAs (DM-miRNAs) and are targeted through the Pearson correlation change on miRNA-target interactions (MTIs), but the efficiency of their method heavily relies on restrictive assumptions. In this paper, a novel method was developed to discover DM-miRNAs using support vector machine (SVM) based on not only the miRNA expression data but also the expression of its regulating target. The application of the new method in breast and kidney cancer datasets found, respectively, 9 and 24 potential DM-miRNAs that cannot be detected by previous methods. Eight and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers, respectively, in existing literature. These results indicate that our new method is more effective in discovering cancer-related miRNAs.
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