ArticleScientific reports2020
miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator.
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 12 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.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of miRNA Targets in Plants.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Short exposure to photo-oxidative damage triggers molecular signals indicative of early retinal degeneration.Frontiers in immunology · 2023Article
- Review
- Micro-RNAs in Human Placenta: Tiny Molecules, Immense Power.Molecules (Basel, Switzerland) · 2022Review
- Article
- Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation.Advances in experimental medicine and biology · 2022Article
- Methods to Investigate miRNA Function: Focus on Platelet Reactivity.Thrombosis and haemostasis · 2021Review
- Integrated microRNA and mRNA signatures associated with overall survival in epithelial ovarian cancer.PloS one · 2021Article
- Expressions of miR-155 and miR-181 and predictions of their structures and targets in pigs (Veterinary world · 2020Article
- Identification of Stably Expressed Reference microRNAs in Epithelial Ovarian Cancer.In vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that regulate gene expression and biological processes through binding to messenger RNAs. Predicting the relationship between miRNAs and their targets is crucial for research and clinical applications. Many tools have been developed to predict miRNA-target interactions, but variable results among the different prediction tools have caused confusion for users. To solve this problem, we developed miRgo, an application that integrates many of these tools. To train the prediction model, extreme values and median values from four different data combinations, which were obtained via an energy distribution function, were used to find the most representative dataset. Support vector machines were used to integrate 11 prediction tools, and numerous feature types used in these tools were classified into six categories-binding energy, scoring function, evolution evidence, binding type, sequence property, and structure-to simplify feature selection. In addition, a novel evaluation indicator, the Chu-Hsieh-Liang (CHL) index, was developed to improve the prediction power in positive data for feature selection. miRgo achieved better results than all other prediction tools in evaluation by an independent testing set and by its subset of functionally important genes. The tool is available at http://predictor.nchu.edu.tw/miRgo.
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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.