Progress and harmonization of gene editing to treat human diseases: Proceeding of COST Action CA21113 GenE-HumDi
Bibliographic record
Abstract
The European Cooperation in Science and Technology (COST) is an intergovernmental organization dedicated to funding and coordinating scientific and technological research in Europe, fostering collaboration among researchers and institutions across countries. Recently, COST Action funded the "Genome Editing to treat Human Diseases" (GenE-HumDi) network, uniting various stakeholders such as pharmaceutical companies, academic institutions, regulatory agencies, biotech firms, and patient advocacy groups. GenE-HumDi's primary objective is to expedite the application of genome editing for therapeutic purposes in treating human diseases. To achieve this goal, GenE-HumDi is organized in several working groups, each focusing on specific aspects. These groups aim to enhance genome editing technologies, assess delivery systems, address safety concerns, promote clinical translation, and develop regulatory guidelines. The network seeks to establish standard procedures and guidelines for these areas to standardize scientific practices and facilitate knowledge sharing. Furthermore, GenE-HumDi aims to communicate its findings to the public in accessible yet rigorous language, emphasizing genome editing's potential to revolutionize the treatment of many human diseases. The inaugural GenE-HumDi meeting, held in Granada, Spain, in March 2023, featured presentations from experts in the field, discussing recent breakthroughs in delivery methods, safety measures, clinical translation, and regulatory aspects related to gene editing.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".