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Record W1658964703 · doi:10.1080/15592294.2015.1039222

Barcelona conference on epigenetics and cancer: 50 years of histone acetylation

2015· article· en· W1658964703 on OpenAlexafffundabout
Montserrat Pérez-Salvia, Laia Simó‐Riudalbas, Juan Ausió, Manel Esteller

Bibliographic record

VenueEpigenetics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsEpigeneticsBiologyHistoneAcetylationChromatinGeneticsLibrary scienceGene

Abstract

fetched live from OpenAlex

The Barcelona Conference on Epigenetics and Cancer (BCEC) was held in Barcelona, Spain, on October 1(st) and 2(nd), 2014. The meeting was co-organized by the Cancer Epigenetics and Biology Program (PEBC-IDIBELL) and B·Debate, an initiative of Biocat, with the support of "la Caixa" Foundation. The scientific committee was comprised of leading scientists in the field of epigenetics: Dr. Manel Esteller, director of PEBC-IDIBELL, Dr. Alejandro Vaquero and Dr. Esteban Ballestar, from PEBC-IDIBELL, Juan Ausió from the University of Victoria (Canada), and Marcus Buschbeck, from the Institute of Predictive and Personalized Medicine of Cancer (IMPPC), as BCEC series coordinator. This meeting was the second edition of the BCEC series, which was launched by 5 leading Barcelonan institutes to bring together leading investigators in the fields of epigenetics and chromatin research. The topics discussed during the meeting included the current challenges, opportunities, and perspectives surrounding the study of histone modifications (focusing in acetylation), chromatin structure and gene expression, and the involvement of histone acetylation in physiology and diseases, such as cancer or neurological diseases.

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 score0.080
Threshold uncertainty score0.703

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 head0.043
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.

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

Citations10
Published2015
Admission routes3
Has abstractyes

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