Barcelona conference on epigenetics and cancer: 50 years of histone acetylation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".