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Record W2794310103 · doi:10.1038/s41416-018-0045-6

New opportunities for kinase drug repurposing and target discovery

2018· letter· en· W2794310103 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBritish Journal of Cancer · 2018
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsnot available
FundersMinistero dello Sviluppo EconomicoFundação de Amparo à Pesquisa do Estado de São PauloOntario Ministry of Economic Development and InnovationDeutschen Konsortium für Translationale KrebsforschungOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsNovartis PharmaWellcome TrustGenome CanadaOntario GenomicsPfizer
KeywordsKinomeDrug repositioningRepurposingDrug discoveryDrugKinaseDrug actionComputational biologyMode of actionDrug developmentPharmacologyBiologyBioinformaticsToxicologyCell biology

Abstract

fetched live from OpenAlex

Protein kinases are major drug targets for oncology. The large size of the kinome, active site conservation and the influence of activation states on drug binding complicates the analysis of their cellular mode of action. In a recent article in Science, Klaeger et al. analysed cellular targets of 243 drug candidates providing a large repository of data for drug repurposing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.279
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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