ArticleTheranostics2018
Tiresias: Context-sensitive Approach to Decipher the Presence and Strength of MicroRNA Regulatory Interactions.
Article in Theranostics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Review
- Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.Cancers · 2022Article
- Simultaneous learning of individual microRNA-gene interactions and regulatory comodules.BMC bioinformatics · 2021Article
- Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- Machine learning analysis of TCGA cancer data.PeerJ. Computer science · 2021Article
- Circulating microRNA trafficking and regulation: computational principles and practice.Briefings in bioinformatics · 2020Review
- Theranostic application ofTheranostics · 2020Article
- AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.BMC bioinformatics · 2019Article
- Non-Coding RNAs and their Integrated Networks.Journal of integrative bioinformatics · 2019Review
- Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets.Nucleic acids research · 2019Article
- A Distributed Classifier for MicroRNA Target Prediction with Validation Through TCGA Expression Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that regulate expression of target messenger RNAs (mRNAs) post-transcriptionally. Understanding the precise regulatory role of miRNAs is of great interest since miRNAs have been shown to play an important role in development, diseases, and other biological processes. Early work on miRNA target prediction has focused on static sequence-driven miRNA-mRNA complementarity. However, recent research also utilizes expression-level data to study context-dependent regulation effects in a more dynamic, physiologically-relevant setting.
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Identifiers
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.