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Epigenetics and Complex Disease: From Etiology to New Therapeutics

2007· review· en· W2135812015 on OpenAlexaff
Carolyn Ptak, Artūras Petronis

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Institute of Mental Health
KeywordsEpigeneticsDiseaseBioinformaticsEpigenesisMedicineEpigenetic therapyComputational biologyBiologyNeuroscienceDNA methylationGeneticsPathologyGene

Abstract

fetched live from OpenAlex

Epigenetics is a new development in complex non-Mendelian disease, which may not only uncover etiologic and pathogenic mechanisms but may also provide the basis for the development of medications that would target the primary epigenetic causes of such diseases. Such epigenetic drugs would be novel, potentially possessing substantially higher therapeutic potential and a much lower rate of adverse effects in comparison to current symptomatic treatments. A collection of epigenetic drugs already exist at various stages of development and, although their effectiveness has yet to be maximized, they show great promise in the treatment of cancer, psychiatric disorders, and other complex diseases. Here we present a review of the epigenetic theory of complex disease and an evaluation of current epigenetic therapies, as well as predictions of the future directions in this expanding field.

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 score0.987
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 studies0.0000.000
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 head0.071
GPT teacher head0.427
Teacher spread0.356 · 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

Citations167
Published2007
Admission routes1
Has abstractyes

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