ArticleScientific reports2020
RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Differently expressed miRNA in plasma samples of immune thrombocytopenic purpura patients and its clinical significance.Journal of medical biochemistry · 2025Article
- Species-specific microRNA discovery and target prediction in the soybean cyst nematode.Scientific reports · 2023Article
- P-TarPmiR accurately predicts plant-specific miRNA targets.Scientific reports · 2023Article
- Review
- mintRULS: Prediction of miRNA-mRNA Target Site Interactions Using Regularized Least Square Method.Genes · 2022Article
- Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI).Scientific reports · 2022Article
- Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering.Scientific reports · 2022Article
- Turning Data to Knowledge: Online Tools, Databases, and Resources in microRNA Research.Advances in experimental medicine and biology · 2022Article
- Glycolysis reprogramming in cancer-associated fibroblasts promotes the growth of oral cancer through the lncRNA H19/miR-675-5p/PFKFB3 signaling pathway.International journal of oral science · 2021Article
- MicroRNAs hsa-miR-618 and hsa-miR-297 Might Modulate the Pleiotropic Effects Exerted by Statins in Endothelial Cells Through the Inhibition of ROCK2 Kinase:Frontiers in cardiovascular medicine · 2021Article
- Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome.PeerJ · 2021Article
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4 authors.
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Abstract
MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA-gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved ([Formula: see text]) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA-gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA-gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and https://doi.org/10.5683/SP2/LD8JKJ.
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