ArticleNon-coding RNA2023
miRinGO: Prediction of Biological Processes Indirectly Targeted by Human microRNAs.
Article in Non-coding RNA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Patient-specific pharmacogenomics demonstrates xCT as predictive therapeutic target in colon cancer with possible implications in tumor connectivity.Molecular oncology · 2026Article
- Temporal Shifts in MicroRNAs Signify the Inflammatory State of Primary Murine Microglial Cells.International journal of molecular sciences · 2025Article
- Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers.Non-coding RNA · 2024Review
- Methods and Tools in RNA Biology.Non-coding RNA · 2023Article
- Evolution of miRNA-Binding Sites and Regulatory Networks in Cichlids.Molecular biology and evolution · 2022Article
- MicroRNAs as Predictors of Future Uterine Malignancy in Endometrial Hyperplasia without Atypia.Journal of personalized medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that are known for their role in the post-transcriptional regulation of target genes. Typically, their functions are predicted by first identifying their target genes and then finding biological processes enriched in these targets. Current tools for miRNA functional analysis use only genes with physical binding sites as their targets and exclude other genes that are indirectly targeted transcriptionally through transcription factors. Here, we introduce a method to predict gene ontology (GO) annotations indirectly targeted by microRNAs. The proposed method resulted in better performance in predicting known miRNA-GO term associations compared to the canonical approach. To facilitate miRNA GO enrichment analysis, we developed an R Shiny application, miRinGO, that is freely available online at GitHub.
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What OpenQuestion holds
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.