ArticleNucleic acids research2017
Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data.
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 14 papers.
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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
14 citing papers in PubMed, 22 citations in OpenAlex.
- miR-195b is required for proper cellular homeostasis in the elderly.Scientific reports · 2024Article
- miRNA Expression Analysis of the Hippocampus in a Vervet Monkey Model of Fetal Alcohol Spectrum Disorder Reveals a Potential Role in Global mRNA Downregulation.Brain sciences · 2023Article
- Web Services for RNA-RNA Interaction Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Review
- MicroRNA Targeting.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases.Frontiers in molecular neuroscience · 2022Review
- Computational Detection of MicroRNA Targets.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Metabolic and energetic benefits of microRNA-22 inhibition.BMJ open diabetes research & care · 2020Article
- Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.BMC bioinformatics · 2020Article
- miRPathDB 2.0: a novel release of the miRNA Pathway Dictionary Database.Nucleic acids research · 2020Article
- Article
- Prediction of the miRNA interactome - Established methods and upcoming perspectives.Computational and structural biotechnology journal · 2020Review
- Freiburg RNA tools: a central online resource for RNA-focused research and teaching.Nucleic acids research · 2018Article
- Long non-coding RNA LNC01133 promotes the tumorigenesis of ovarian cancer by sponging miR-126.International journal of clinical and experimental pathology · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms. Simultaneously, the inverse dataset of negative interactions not likely to occur was extracted to increase classifier performance, as measured using an expansive set of experimentally validated interactions from a variety of sources. In a second differential mode, candidate miRNAs are predicted by indicating genes to be targeted and others to be avoided to potentially increase specificity of results. As an example, we investigate the neural crest, a transient structure in vertebrate development where miRNAs play a pivotal role. Patterns of metaMIR-predicted miRNA regulation alone partially recapitulated functional relationships among genes, and separate differential analysis revealed miRNA candidates that would downregulate components implicated in cancer progression while not targeting tumour suppressors. Such an approach could aid in therapeutic application of miRNAs to reduce unintended effects. The utility is available at http://rna.informatik.uni-freiburg.de/metaMIR/.
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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.