The relationship between TRAF6 and tumors
Bibliographic record
Abstract
Abstract Tumor necrosis factor receptor (TNFR)-related factors (TRAFs) are important linker molecules in the tumor necrosis factor superfamily (TNFSF) and the Toll-like/interleukin-1 receptor (TLR/ILR) superfamily. There are seven members: TRAF1-TRAF7, among those members, tumor necrosis factor receptor-associated factor 6 (TRAF6) is upregulated in various tumors, which has been related to tumorigenesis and development. With the in-depth study of the relationship between TRAF6 and different types of tumors,TRAF6has oncogenic characteristics involved in tumorigenesis, tumor development, invasion, and metastasis through various signaling pathways, therefore, targeting TRAF6 has provided a novel strategy for tumor treatment. This review summarizes and analyzes the role of TRAF6 in tumorigenesis and tumor development in combination with the current research on TRAF6 and tumors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".