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Record W3082596433 · doi:10.3390/epigenomes4030018

New Aspects of the Epigenetics of Pancreatic Carcinogenesis

2020· article· en· W3082596433 on OpenAlexaff
Murat Törüner, Martín E. Fernández-Zapico, Christopher L. Pin

Bibliographic record

VenueEpigenomes · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsChildren's Hospital of Western OntarioChildren’s Health Research InstituteWestern University
FundersNational Cancer Institute
KeywordsEpigeneticsPancreatic cancerPathogenesisCarcinogenesisBiologyDNA methylationMalignancyHistoneCancerDiseaseEpigenesisBioinformaticsGeneticsCancer researchMedicineImmunologyGeneInternal medicineGene expression

Abstract

fetched live from OpenAlex

Pancreatic cancer remains among the deadliest forms of cancer with a 5 year survival rate less than 10%. With increasing numbers being observed, there is an urgent need to elucidate the pathogenesis of pancreatic cancer. While both contribute to disease progression, neither genetic nor environmental factors completely explain susceptibility or pathogenesis. Defining the links between genetic and environmental events represents an opportunity to understand the pathogenesis of pancreatic cancer. Epigenetics, the study of mitotically heritable changes in genome function without a change in nucleotide sequence, is an emerging field of research in pancreatic cancer. The main epigenetic mechanisms include DNA methylation, histone modifications and RNA interference, all of which are altered by changes to the environment. Epigenetic mechanisms are being investigated to clarify the underlying pathogenesis of pancreatic cancer including an increasing number of studies examining the role as possible diagnostic and prognostic biomarkers. These mechanisms also provide targets for promising new therapeutic approaches for this devastating malignancy.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.360

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 head0.015
GPT teacher head0.228
Teacher spread0.212 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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