Notch signaling in development and disease
Bibliographic record
Abstract
Notch receptors and ligands were first identified in flies and worms, where they were shown to regulate cell proliferation, cell differentiation, and, in particular, binary cell fate decisions in a variety of developmental contexts. The first mammalian Notch homolog was discovered to be a partner in a chromosomal translocation in a subset of human T-cell leukemias. Subsequent studies in mice and humans have shown that Notch signaling plays essential roles at multiple stages of hematopoiesis, and also regulates the development or homeostasis of cells in many tissues and organs. Thus, it is not surprising that mutations which disrupt Notch signaling cause a wide range of cancers and developmental disorders. Perhaps because it is so widely used, Notch signaling is subject to many unusual forms of regulation. In this review, we will first outline key aspects of Notch signaling and its regulation by endocytosis, glycosylation, and ubiquitination. We will then overview recent literature elucidating how Notch regulates cell-lineage decisions in a variety of developmental contexts. Finally, we will describe the roles of dysregulated Notch signaling in causing several types of cancer and other pathologies.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".