MétaCan
Menu
Back to cohort
Record W2005895632 · doi:10.1038/nature10983

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

2012· article· en· W2005895632 on OpenAlexafffund
Christina Curtis, Sohrab P. Shah, Suet‐Feung Chin, Gulisa Turashvili, Oscar M. Rueda, Mark Dunning, Doug Speed, Andy G. Lynch, Shamith Samarajiwa, Yinyin Yuan, Stefan Gräf, Gavin Ha, Gholamreza Haffari, Ali Bashashati, Roslin Russell, Steven McKinney, Anita Langerød, Andrew R. Green, Elena Provenzano, Gordon Wishart, Sarah E. Pinder, Peter H. Watson, Florian Markowetz, Leigh C. Murphy, Ian O. Ellis, Arnie Purushotham, Anne‐Lise Børresen‐Dale, James D. Brenton, Simon Tavaré, Carlos Caldas, Samuel Aparício

Bibliographic record

VenueNature · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of ManitobaBC Cancer AgencyUniversity of British Columbia
FundersNIHR Cambridge Biomedical Research CentreBC Cancer AgencyKing's College LondonUniversity of Southern CaliforniaNational Institute for Health and Care ResearchDivision of Mathematical SciencesNational Institutes of HealthUniversity of CambridgeCancer Research UKNational Human Genome Research InstituteMichael Smith Health Research BC
KeywordsBiologyTranscriptomeBreast cancerGeneCopy-number variationGeneticsComparative genomic hybridizationCopy number analysisGenomeComputational biologyGene expression profilingPopulationCancerGene expressionMedicine

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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

Citations6,380
Published2012
Admission routes2
Has abstractno

Explore more

Same venueNatureSame topicCancer Genomics and DiagnosticsFrench-language works237,207