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Record W2885739537 · doi:10.1080/15592294.2018.1464351

Epigenetics of gastrointestinal diseases: notes from a workshop

2018· article· en· W2885739537 on OpenAlexaff
David L. Marks, Rachel Olson, Raúl Urrutia, Daniel D. Billadeau, Nilotpal Roy, George A. Călin, Muller Fabbri, Μαρίνα Κουτσιούμπα, Dimitrios Iliopoulos, Tamás Ördög, Robert C. Huebert, Olga F. Sarmento, Adebowale O. Bamidele, William A. Faubion, Gwen Lomberk, Jens T. Siveke, Nita Ahuja, Juan Iovanna, Ryan A. Hlady, Keith D. Robertson, John B. Kisiel, Christopher L. Pin, Martín E. Fernández-Zapico

Bibliographic record

VenueEpigenetics · 2018
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsChildren’s Health Research InstituteWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Institutes of Health
KeywordsEpigeneticsBiologyDNA methylationDiseaseBioinformaticsComputational biologyGeneticsMedicinePathologyGene

Abstract

fetched live from OpenAlex

International experts gathered at the Mayo Clinic (Rochester MN, USA) on February 27th-28th, 2017 for a meeting entitled 'Basic and Translational Facets of the Epigenetics of GI Diseases'. This workshop summarized recent advances on the role of epigenetics in the pathobiology of gastrointestinal (GI) diseases. Highlights of the meeting included recent advances on the involvement of different epigenetic mechanisms in malignant and nonmalignant GI disorders and the epigenetic heterogeneity exhibited in these diseases. The translational value of epigenetic drugs, as well as the current and future use of epigenetic changes (i.e., DNA methylation patterns) as biomarkers for early detection tools or disease stratification were also important topics of discussion.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.044
GPT teacher head0.354
Teacher spread0.311 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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