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Record W1890648625 · doi:10.3390/biom5042935

The RNA Splicing Response to DNA Damage

2015· review· en· W1890648625 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomolecules · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsRNA splicingAlternative splicingSplicing factorDNA damageRibonucleoproteinBiologyCell biologyDNA repairRNAMinigeneGeneticsExonic splicing enhancerSR proteinDNAGeneComputational biologyExon

Abstract

fetched live from OpenAlex

The number of factors known to participate in the DNA damage response (DDR) has expanded considerably in recent years to include splicing and alternative splicing factors. While the binding of splicing proteins and ribonucleoprotein complexes to nascent transcripts prevents genomic instability by deterring the formation of RNA/DNA duplexes, splicing factors are also recruited to, or removed from, sites of DNA damage. The first steps of the DDR promote the post-translational modification of splicing factors to affect their localization and activity, while more downstream DDR events alter their expression. Although descriptions of molecular mechanisms remain limited, an emerging trend is that DNA damage disrupts the coupling of constitutive and alternative splicing with the transcription of genes involved in DNA repair, cell-cycle control and apoptosis. A better understanding of how changes in splice site selection are integrated into the DDR may provide new avenues to combat cancer and delay aging.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.974
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.375
Teacher spread0.331 · 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