Best evidence linking the extracellular factor TGF-β to cancer-associated alternative splicing programs
Bibliographic record
Abstract
Alternative splicing is a mechanism by which several RNA transcripts can be created from one gene. Splicing factors are RNA binding proteins recognizing cis-acting sequences that positively or negatively influence the splicing decision based on their relative position to the splice site and identity. However, few studies have focused on the regulation of splicing factors, and even less on the regulation of alternative splicing from extracellular factors. Transforming growth factor beta 1 (TGF-β) is a well study extracellular factors regulating multiple cancer-associated cell phenotype (apoptosis, epithelial to mesenchymal transition, angiogenesis, differentiation into cancer-associated fibroblasts) in a cell type-dependent manner. Intriguingly, there is examples of alternative splicing variants and/or their regulatory splicing factors influencing each of these hallmarks in vitro. Here, we provide the best evidence suggesting that TGF-β may drive cancer-associated alternative splicing programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".