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Record W6887936744 · doi:10.17863/cam.25922

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.

2018· article· en· W6887936744 on OpenAlexfundno aff

Bibliographic record

VenueApollo (University of Cambridge) · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
FundersMedical Research and Materiel CommandServicio Gallego de SaludInstituto de Salud Carlos IIICancer Council TasmaniaCancer Council South AustraliaNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchCenters for Disease Control and PreventionImperial Experimental Cancer Medicine CentreNational Institutes of HealthHellenic Health FoundationDivision of Cancer Epidemiology and Genetics, National Cancer InstituteFreistaat SachsenDeutschen Konsortium für Translationale KrebsforschungXunta de GaliciaMutuelle Générale de l'Education NationaleInstitut Gustave-RoussyMinistero dello Sviluppo EconomicoDeutsche KrebshilfeMedizinischen Hochschule HannoverKWF KankerbestrijdingStockholms Läns LandstingLigue Contre le CancerKuopion Yliopistollinen SairaalaKarolinska InstitutetUniversity of CambridgeGovernment of CanadaMinisterio de Sanidad, Servicios Sociales e IgualdadUniversitätsklinikum Hamburg-EppendorfRussian Foundation for Basic ResearchInstitut National de la Santé et de la Recherche MédicaleRobert Bosch StiftungU.S. ArmyUniversity of WestminsterCancer Council Western AustraliaEuropean CommissionEuropean Regional Development FundKing's College LondonCancerfondenNational Cancer InstituteUniversity College LondonCancer Institute NSWNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchBreast Cancer CampaignEberhard Karls Universität TübingenFondation du cancer du sein du QuébecGénome QuébecOvarian Cancer Research FundBundesministerium für Bildung und ForschungNational Breast Cancer FoundationSwedish Cancer FoundationAgency for Science, Technology and ResearchLon V. Smith FoundationMinistère du Développement Économique, de l’Innovation et de l’ExportationDeutsche Gesetzliche UnfallversicherungUniversity of California, IrvineDavid F. and Margaret T. Grohne Family FoundationNational Heart, Lung, and Blood InstituteItä-Suomen YliopistoGenome CanadaDeutsches KrebsforschungszentrumVanderbilt University Medical CenterDr. Ralph and Marian Falk Medical Research TrustBreast Cancer Research FoundationMcGill UniversityWorld Cancer Research FundHelsingin ja Uudenmaan SairaanhoitopiiriCancer Research UKAssociazione Italiana per la Ricerca sul CancroBeckman Research Institute, City of HopeStavros Niarchos FoundationUniversity of Southern CaliforniaRheinische Friedrich-Wilhelms-Universität BonnOak FoundationAvon Foundation for WomenCancer Council NSWSusan G. Komen for the CureVirginia Department of HealthNational Institute for Health and Care ResearchDivision of Cancer Prevention, National Cancer InstituteVanderbilt UniversityCancer Council VictoriaCalifornia Department of Public HealthU.S. Department of Health and Human Services
KeywordsBreast cancerGeneGenetic associationGenome-wide association studyCandidate geneCancerGene expressionGenetic variantsCase-control studyHuman genetics

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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

Citations0
Published2018
Admission routes1
Has abstractno

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