The MAGE proteins: Emerging roles in cell cycle progression, apoptosis, and neurogenetic disease
Bibliographic record
Abstract
Since the identification of the first MAGE gene in 1991, the MAGE family has expanded dramatically, and over 25 MAGE genes have now been identified in humans. The focus of studies on the MAGE proteins has been their potential for cancer immunotherapy, as a result of the finding that peptides derived from MAGE gene products are bound by major histocompatibility complexes and presented on the cell surface of cancer cells. However, the normal physiological role of MAGE proteins has remained a mystery. Recent studies are now beginning to provide insights into MAGE gene function. Necdin acts as a cell cycle regulatory protein and plays a key role in the pathogenesis of Prader-Willi syndrome, a neurogenetic disorder. MAGE-D1, identified as a binding partner for the p75 neurotrophin receptor, the apoptosis inhibitory protein XIAP, and Dlx/MSX homeodomain proteins, blocks cell cycle progression and enhances apoptosis. This review provides an overview of the human MAGE genes and proteins, summarizes recent findings on their cellular roles, and provides a baseline for future studies on this intriguing gene family.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".