ArticleCancer informatics2014
A Bayesian Framework to Improve MicroRNA Target Prediction by Incorporating External Information.
Article in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 13 citations in OpenAlex.
- Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients.British journal of cancer · 2020Article
- Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles.PloS one · 2016Article
- Integrating full spectrum of sequence features into predicting functional microRNA-mRNA interactions.Bioinformatics (Oxford, England) · 2015Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
MicroRNAs (miRNAs) are small regulatory RNAs that play key gene-regulatory roles in diverse biological processes, particularly in cancer development. Therefore, inferring miRNA targets is an essential step to fully understanding the functional properties of miRNA actions in regulating tumorigenesis. Bayesian linear regression modeling has been proposed for identifying the interactions between miRNAs and mRNAs on the basis of the integrated sequence information and matched miRNA and mRNA expression data; however, this approach does not use the full spectrum of available features of putative miRNA targets. In this study, we integrated four important sequence and structural features of miRNA targeting with paired miRNA and mRNA expression data to improve miRNA-target prediction in a Bayesian framework. We have applied this approach to a gene-expression study of liver cancer patients and examined the posterior probability of each miRNA-mRNA interaction being functional in the development of liver cancer. Our method achieved better performance, in terms of the number of true targets identified, than did other methods.
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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.