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Record W4392967038 · doi:10.3389/fmolb.2024.1374843

Regulatory role of RNA-binding proteins in microRNA biogenesis

2024· article· en· W4392967038 on OpenAlexafffund
Claire Hynes, Pavan Kumar Kakumani

Bibliographic record

VenueFrontiers in Molecular Biosciences · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandUniversities Space Research AssociationCancer Research Society
KeywordsDroshaDicermicroRNABiologyBiogenesisGene silencingRNA-binding proteinArgonauteUntranslated regionRegulation of gene expressionCell biologyGene expressionRNAPost-transcriptional regulationComputational biologyGeneticsRNA interferenceGene

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are small non-coding RNAs that silence gene expression through their interaction with complementary sequences in the 3' untranslated regions (UTR) of target mRNAs. miRNAs undergo a series of steps during their processing and maturation, which are tightly regulated to fine-tune their abundance and ability to function in post-transcriptional gene silencing. miRNA biogenesis typically involves core catalytic proteins, namely, Drosha and Dicer, and several other RNA-binding proteins (RBPs) that recognize and interact with miRNA precursors and/or their intermediates, and mature miRNAs along with their interacting proteins. The series of RNA-protein and protein-protein interactions are critical to maintaining miRNA expression levels and their function, underlying a variety of cellular processes. Throughout this article, we review RBPs that play a role in miRNA biogenesis and focus on their association with components of the miRNA pathway with functional consequences in the processing and generation of mature miRNAs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations63
Published2024
Admission routes2
Has abstractyes

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