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Record W4362657804 · doi:10.1007/s00439-023-02544-2

DNA methylation signatures for chromatinopathies: current challenges and future applications

2023· article· en· W4362657804 on OpenAlexaff
Zain Awamleh, Sarah J. Goodman, Sanaa Choufani, Rosanna Weksberg

Bibliographic record

VenueHuman Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersSimons Foundation Autism Research Initiative
KeywordsBiologyDNA methylationEpigeneticsGeneticsHuman geneticsMethylationRNA-Directed DNA MethylationDNAComputational biologyPhenotypeGeneGene expression

Abstract

fetched live from OpenAlex

Pathogenic variants in genes that encode epigenetic regulators are the cause for more than 100 rare neurodevelopmental syndromes also termed "chromatinopathies". DNA methylation signatures, syndrome-specific patterns of DNA methylation alterations, serve as both a research avenue for elucidating disease pathophysiology and a clinical diagnostic tool. The latter is well established, especially for the classification of variants of uncertain significance (VUS). In this perspective, we describe the seminal DNA methylation signature research in chromatinopathies; the complex relationships between genotype, phenotype and DNA methylation, and the future applications of DNA methylation signatures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.318
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations17
Published2023
Admission routes1
Has abstractyes

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