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Record W2964117905 · doi:10.1038/s41422-019-0203-2

Forkhead domain mutations in FOXA1 drive prostate cancer progression

2019· letter· en· W2964117905 on OpenAlexafffund
Shuai Gao, Sujun Chen, Dong Han, David Barrett, Wanting Han, Musaddeque Ahmed, Susan Patalano, Jill A. Macoska, Housheng Hansen He, Changmeng Cai

Bibliographic record

VenueCell Research · 2019
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFOXO transcription factor regulation
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersCIHR Skin Research Training CentreNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of California, San FranciscoCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaU.S. Department of Health and Human ServicesNational Institutes of HealthTerry Fox FoundationNational Institute of Diabetes and Digestive and Kidney DiseasesProstate Cancer CanadaU.S. Department of DefensePrincess Margaret Cancer FoundationMassachusetts General HospitalGovernment of Canada
KeywordsBiologyFOXA1Prostate cancerForkhead Transcription FactorsCancer researchDomain (mathematical analysis)GeneticsMutationCancerComputational biologyBioinformaticsGeneRegulation of gene expressionBreast cancer

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.041
GPT teacher head0.378
Teacher spread0.338 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations40
Published2019
Admission routes2
Has abstractno

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