ING function in apoptosis in diverse model systemsThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB’s 51st Annual Meeting – Epigenetics and Chromatin Dynamics, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Genetic studies in model organisms have shown that programmed cell death (apoptosis) plays a significant role during development, where a deficiency in apoptosis results in severe and diverse diseases. Dysregulation of apoptosis also contributes to a variety of human diseases, such as cancer and autoimmune diseases. ING family proteins (ING1-ING5) are involved in many cellular processes, and appear to play a significant role in apoptosis. Loss or downregulation of ING protein function is frequently observed in different tumour types, many of which are resistant to apoptosis, thus warranting their classification as type II tumour suppressors. Several different in vitro and in vivo models have explored the role of ING proteins in regulating apoptosis. In this review, we discuss the progress that has been made in understanding ING protein function in apoptosis using in vitro studies and Mus musculus, Xenopus laevis, and Caenorhabditis elegans experimental models, with an emphasis on ING1 and ING3.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".