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Record W2811111314 · doi:10.1101/cshperspect.a032896

Translational Control in Cancer

2018· review· en· W2811111314 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

VenueCold Spring Harbor Perspectives in Biology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Institutes of HealthDeutsches KrebsforschungszentrumCanadian Institutes of Health ResearchNew York State Department of HealthBreast Cancer Research FoundationSusan G. Komen for the CureHoward Hughes Medical Institute
KeywordsCenter (category theory)BiologyCancerLibrary scienceTranslational researchGerontologyGeneticsMedicineComputer scienceBiotechnology

Abstract

fetched live from OpenAlex

The translation of messenger RNAs (mRNAs) into proteins is a key event in the regulation of gene expression. This is especially true in the cancer setting, as many oncogenes and transforming events are regulated at this level. Cancer-promoting factors that are translationally regulated include cyclins, antiapoptotic factors, proangiogenic factors, regulators of cell metabolism, prometastatic factors, immune modulators, and proteins involved in DNA repair. This review discusses the diverse means by which cancer cells deregulate and reprogram translation, and the resulting oncogenic impacts, providing insights into the complexity of translational control in cancer and its targeting for cancer therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.036
GPT teacher head0.379
Teacher spread0.343 · 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