Editorial: Molecular switches of the immune system: The E-protein/Id axis in hematopoietic development and function
Bibliographic record
Abstract
Molecular switches of the immune system: The E protein/Id axis in hematopoietic development and function This Frontiers in Immunology Research Topic is composed by a set of review and original articles, all highlighting the roles of E-proteins in hematopoiesis.The first evidence for a role of E-box binding (E) proteins in the immune system came from the identification of protein bound "E-boxes" in regulatory elements at the immunoglobulin locus (1, 2).The subsequent identification of these DNA binding proteins (3) revealed amino-acid homology to MyoD as well as Myc (3, 4), and created the foundation for a new research area in molecular immunology.The complexity of the regulatory networks involving these "basic-helix-loop-helix" (bHLH) transcription factors became obvious, as both broadly expressed and tissue specific family members became identified (4).These proteins are able to form homo-as well as hetero-dimeric complexes targeting E-boxes in transcription regulatory elements of both ubiquitously expressed and tissue restricted genes (4-6).Among the broadly expressed prot*ins are E12, E47 [both encoded by the E2A gene (TCF3) (3)], HEB (TCF12) (7) and E2-2 (TCF4) (8), that due to their overlapping activities and dimerization patterns have been denoted as E proteins [Reviewed in (9)].In aggregate, E proteins display a high degree of redundancy, and it has been proposed that functional dose, rather than expression of any specific protein, controls developmental trajectories in hematopoiesis (10-13). The complexity of the E protein regulatory network increased with the identification of Inhibitor of DNA binding (ID) proteins (14). The ID proteins harbor the HLH domainFrontiers in Immunology frontiersin.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".