MétaCan
Menu
Back to cohort

Regulation of mRNA Translation and Stability by microRNAs

2010· review· en· W2163106706 on OpenAlexafffund
Marc R. Fabian, Nahum Sonenberg, Witold Filipowicz

Bibliographic record

VenueAnnual Review of Biochemistry · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchEuropean CommissionMcGill University
KeywordsmicroRNATranslation (biology)Psychological repressionMessenger RNAUntranslated regionBiologyPost-transcriptional regulationTranslational regulationRNA-binding proteinThree prime untranslated regionCell biologyArgonauteFunction (biology)Regulation of gene expressionGene expressionRNAComputational biologyGeneticsRNA interferenceGene

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are small noncoding RNAs that extensively regulate gene expression in animals, plants, and protozoa. miRNAs function posttranscriptionally by usually base-pairing to the mRNA 3'-untranslated regions to repress protein synthesis by mechanisms that are not fully understood. In this review, we describe principles of miRNA-mRNA interactions and proteins that interact with miRNAs and function in miRNA-mediated repression. We discuss the multiple, often contradictory, mechanisms that miRNAs have been reported to use, which cause translational repression and mRNA decay. We also address the issue of cellular localization of miRNA-mediated events and a role for RNA-binding proteins in activation or relief of miRNA repression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.327
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3,279
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueAnnual Review of BiochemistrySame topicRNA Research and SplicingFrench-language works237,207