Genetic variants of the MAPK pathways: In silico characterization to breast cancer association
Bibliographic record
Abstract
The MAPK pathways play a critical role in cancer development, particularly breast cancer. Only 5-10% of breast cancer cases can be explained genetically, thus leaving a large portion of cancers that may be explained by multiple variants such as SNPs; they are abundant and are known to alter breast cancer risk. Thus SNPs of the MAPK pathways may be important in breast cancer risk. In silico characterization of SNPs from the MAPK pathways revealed that more than 50% of the SNPs studied changed evolutionarily conserved amino acids or putatively altered phosphorylation patterns. A case-control study of these variants revealed SP1-A750P showed a trend towards increased breast cancer risk, however was not statistically significant, MYC N11S, previously shown to increase breast cancer risk, was not confirmed by this study and the NFkappaB1-M507V heterozygote variant was associated with a 73% decreased breast cancer risk (OR= 0.27, 95%CI= 0.08-0.95, P=0.03).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".