ArticleNucleic acids research2017
miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Toehold-VISTA: a machine learning approach to decipher programmable RNA sensor-target interactions.Nucleic acids research · 2026Article
- Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks.Journal of chemical information and modeling · 2025Article
- Fibroblast activation protein: Pivoting cancer/chemotherapeutic insight towards heart failure.Biochemical pharmacology · 2024Review
- Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes.Frontiers in pharmacology · 2023Article
- Review
- Computational Detection of MicroRNA Targets.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.BMC bioinformatics · 2020Article
- Prediction of the miRNA interactome - Established methods and upcoming perspectives.Computational and structural biotechnology journal · 2020Review
- miRNAtools: Advanced Training Using the miRNA Web of Knowledge.Non-coding RNA · 2018Article
- mirDIP 4.1-integrative database of human microRNA target predictions.Nucleic acids research · 2018Article
- Article
- A high-throughput 3' UTR reporter screening identifies microRNA interactomes of cancer genes.PloS one · 2018Article
- T-ALL and thymocytes: a message of noncoding RNAs.Journal of hematology & oncology · 2017Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In microRNA (miRNA) target prediction, typically two levels of information need to be modeled: the number of potential miRNA binding sites present in a target mRNA and the genomic context of each individual site. Single model structures insufficiently cope with this complex training data structure, consisting of feature vectors of unequal length as a consequence of the varying number of miRNA binding sites in different mRNAs. To circumvent this problem, we developed a two-layered, stacked model, in which the influence of binding site context is separately modeled. Using logistic regression and random forests, we applied the stacked model approach to a unique data set of 7990 probed miRNA-mRNA interactions, hereby including the largest number of miRNAs in model training to date. Compared to lower-complexity models, a particular stacked model, named miSTAR (miRNA stacked model target prediction; www.mi-star.org), displays a higher general performance and precision on top scoring predictions. More importantly, our model outperforms published and widely used miRNA target prediction algorithms. Finally, we highlight flaws in cross-validation schemes for evaluation of miRNA target prediction models and adopt a more fair and stringent approach.
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