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Record W2560510482 · doi:10.1038/ng.3721

Disease variants alter transcription factor levels and methylation of their binding sites

2016· article· en· W2560510482 on OpenAlexaff
Marc Jan Bonder, René Luijk, Daria V. Zhernakova, Matthijs Moed, Patrick Deelen, Martijn Vermaat, Maarten van Iterson, Freerk van Dijk, Michiel van Galen, Jan Bot, Roderick C. Slieker, P. Mila Jhamai, Michaël Verbiest, H. Eka D. Suchiman, Marijn Verkerk, Ruud van der Breggen, Jeroen van Rooij, Nico Lakenberg, Wibowo Arindrarto, Szymon M. Kiełbasa, Iris H. Jonkers, Peter van ‘t Hof, Irene Nooren, Marian Beekman, Joris Deelen, Alexandra Zhernakova, Ettje F. Tigchelaar, Morris A. Swertz, Albert Hofman, André G. Uitterlinden, René Pool, Jenny van Dongen, Jouke‐Jan Hottenga, Coen D.A. Stehouwer, Carla Kallen, Casper G. Schalkwijk, Leonard H. van den Berg, Erik W. van Zwet, Hailiang Mei, Yang Li, Mathieu Lemire, Thomas J. Hudson, P. Eline Slagboom, Cisca Wijmenga, Jan H. Veldink, Marleen M. J. van Greevenbroek, Cornelia M. van Duijn, Dorret I. Boomsma, Aaron Isaacs, Rick Jansen, Joyce B. J. van Meurs, Peter A.C. ’t Hoen, Lude Franke, Bastiaan T. Heijmans

Bibliographic record

VenueNature Genetics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekZonMw
KeywordsBiologyDNA methylationGeneticsMethylationSingle-nucleotide polymorphismDNA binding siteCpG siteExpression quantitative trait lociQuantitative trait locusDifferentially methylated regionsTranscription factorCTCFGenome-wide association studyComputational biologyGenePromoterGene expressionGenotypeEnhancer

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations564
Published2016
Admission routes1
Has abstractno

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