MétaCan
Menu
Back to cohort
Record W2084376878 · doi:10.4155/fmc.11.150

Eukaryotic Initiation Factor 4F: A Vulnerability of Tumor Cells

2011· review· en· W2084376878 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.

Bibliographic record

VenueFuture Medicinal Chemistry · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsEIF4EEIF4GInitiation factoreIF4AEukaryotic initiation factorEIF4A1BiologyEukaryotic translationCell biologyEukaryotic translation initiation factor 4 gammaeIF2EIF4EBP1Protein biosynthesisHelicaseIntegrated stress responseTranslational regulationRibosomeTranslation (biology)Messenger RNAGeneticsRNAGene

Abstract

fetched live from OpenAlex

Protein synthesis is a complex, tightly regulated process in eukaryotic cells and its deregulation is a hallmark of many cancers. Translational control occurs primarily at the rate-limiting initiation step, where ribosomal subunits are recruited to template mRNAs through the concerted action of several eukaryotic initiation factors (eIFs). One factor that interacts with both the mRNA and ribosomes, and appears limiting for translation is eIF4F, a complex composed of the cap-binding protein, eIF4E; the scaffold protein, eIF4G; and the ATP-dependent DEAD-box helicase, eIF4A. eIF4E appears to play an important role in tumor initiation and progression since its overexpression can cooperate with oncogenes to accelerate transformation in cell lines and animal models, and its levels are elevated in many human cancers. This, therefore, represents a vulnerability for transformed cells, and presents an opportunity for therapeutic intervention. In this review, we discuss approaches for targeting eIF4F activity.

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 score0.941
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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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