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Record W4280537263 · doi:10.1002/wrna.1738

Targeting <scp>DEAD</scp>‐box <scp>RNA</scp> helicases: The emergence of molecular staples

2022· review· en· W4280537263 on OpenAlexafffund
Sai Kiran Naineni, Françis Robert, Bhushan Nagar, Jerry Pelletier

Bibliographic record

VenueWiley Interdisciplinary Reviews - RNA · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsRNAHelicaseeIF4ABiologyNon-coding RNAComputational biologyRNA-binding proteinRNA Helicase AFunction (biology)Transcription (linguistics)Cell biologyRiboswitchGeneGenetics

Abstract

fetched live from OpenAlex

RNA helicases constitute a large family of proteins that play critical roles in mediating RNA function. They have been implicated in all facets of gene expression pathways involving RNA, from transcription to processing, transport and translation, and storage and decay. There is significant interest in developing small molecule inhibitors to RNA helicases as some family members have been documented to be dysregulated in neurological and neurodevelopment disorders, as well as in cancers. Although different functional properties of RNA helicases offer multiple opportunities for small molecule development, molecular staples have recently come to the forefront. These bifunctional molecules interact with both protein and RNA components to lock them together, thereby imparting novel gain-of-function properties to their targets. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

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 score0.003
Threshold uncertainty score0.010

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.046
GPT teacher head0.357
Teacher spread0.311 · 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

Citations26
Published2022
Admission routes2
Has abstractyes

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