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Epigenetics, DNA Methylation, and Chromatin Modifying Drugs

2008· review· en· W2169099940 on OpenAlexaff
Moshe Szyf

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpigeneticsDNA methylationHistoneBiologyEpigenetic regulation of neurogenesisChromatinEpigenetic therapyCancer epigeneticsEpigenomicsGeneticsGeneDiseaseChromatin remodelingComputational biologyGene expressionMedicineHistone methyltransferase

Abstract

fetched live from OpenAlex

Evidence is emerging that several diseases and behavioral pathologies result from defects in gene function. The best-studied example is cancer, but other diseases such as autoimmune disease, asthma, type 2 diabetes, metabolic disorders, and autism display aberrant gene expression. Gene function may be altered by either a change in the sequence of the DNA or a change in epigenetic programming of a gene in the absence of a sequence change. With epigenetic drugs, it is possible to reverse aberrant gene expression profiles associated with different disease states. Several epigenetic drugs targeting DNA methylation and histone deacetylation enzymes have been tested in clinical trials. Understanding the epigenetic machinery and the differential roles of its components in specific disease states is essential for developing targeted epigenetic therapy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.362
Teacher spread0.337 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations376
Published2008
Admission routes1
Has abstractyes

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