SnoN: Bridging Neurobiology and Cancer Biology
Bibliographic record
Abstract
The transcriptional regulator SnoN has been the subject of growing interest due to its diverse functions in normal and pathological settings. A large body of evidence has established a fundamental role for SnoN as a modulator of signaling and responses by the transforming growth beta (TGFβ) family of cytokines, though how SnoN regulates TGFβ responses remains incompletely understood. In accordance with the critical and complex roles of TGFβ in tumorigenesis and metastasis, SnoN may act as a tumor promoter or suppressor depending on the stage and type of cancer. Beyond its role in cancer, SnoN has also been implicated in the control of axon morphogenesis in postmitotic neurons in the mammalian brain. Remarkably, signaling pathways that control SnoN functions in the divergent cycling cells and postmitotic neurons appear to be conserved. Identification of novel SnoN regulatory and effector mechanisms holds the promise of advances at the interface of cancer biology and neurobiology. Keywords: SnoN, ING2, Ccd1, TGF-β, Smad, signaling, transcription control, cell cycle, axonal growth, cancer, TGF-b, Homeostasis, Histone deacetylase, Plant homeodomain, Tripartite complex, Oncogene, Tumorigenesis, Barrett's esophagus, Ski, Neurobiology, Metastasis
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".