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Record W2098052125 · doi:10.1016/j.ccr.2013.10.006

Reduced H3K27me3 and DNA Hypomethylation Are Major Drivers of Gene Expression in K27M Mutant Pediatric High-Grade Gliomas

2013· article· en· W2098052125 on OpenAlexaff
Sebastian Bender, Yujie Tang, Anders M. Lindroth, Volker Hovestadt, David Jones, Marcel Kool, Marc Zapatka, Paul A. Northcott, Dominik Sturm, Wei Wang, Bernhard Radlwimmer, Jonas W. Højfeldt, Nathalène Truffaux, David Castel, Simone Schubert, Marina Ryzhova, Huriye Seker‐Cin, Jan Gronych, Pascal D. Johann, Sebastian Stark, Jochen Meyer, Till Milde, Martin U. Schuhmann, Martin Ebinger, Camelia‐Maria Monoranu, Anitha Ponnuswami, Spenser Chen, Chris Jones, Olaf Witt, V. Peter Collins, Andreas von Deimling, Nada Jabado, Stéphanie Puget, Jacques Grill, Kristian Helin, Andrey Korshunov, Peter Lichter, Michelle Monje, Christoph Plass, Yoon-Jae Cho, Stefan M. Pfister

Bibliographic record

VenueCancer Cell · 2013
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcGill University Health Centre
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthDeutsche KrebshilfeMcKenna Claire FoundationDeutsches KrebsforschungszentrumAlex's Lemonade Stand Foundation for Childhood CancerNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchBundesministerium für Bildung und ForschungCure Starts Now FoundationSt. Baldrick's Foundation
KeywordsBiologyHistoneChromatin immunoprecipitationMutantEpigeneticsChromatinEZH2MethyltransferaseHistone H3DNA methylationPRC2GeneGeneticsMutationGene expressionMethylationCell biologyCancer researchPromoter

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 score0.002
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations842
Published2013
Admission routes1
Has abstractno

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