Bibliographic record
Abstract
Variation in gene coding sequence represents a significant factor in predisposition to disease, including cancer. Variants of some DNA repair genes (e.g. MLH1, MSH2 and MSH6) are known to predispose to cancer. We identified single nucleotide polymorphisms (SNPs) in five DNA repair genes in 142 healthy individuals using a DNA sequencing protocol optimized for the direct detection of single nucleotide polymorphisms. This approach, called the heterozygote sequencing protocol (HSP), enables moderate-scale population surveys of SNPs. HSP uses fluorescently tagged primers and exploits the large dynamic range and low background of automated fluorescent sequencing. HSP may be used for any sequence that can be amplified by PCR. A total of 12 SNP variants in MGMT, ERCC1, CDK7, CCNH and XRCC4 were identified, 11 at polymorphic frequencies, with an average frequency of 0.22 (95% confidence interval 0.20-0.24). Among the 82 individuals for whom complete SNP profiles were available, no one person carried the GenBank reference sequence for all five genes. The extensive heterogeneity observed in these five genes is intriguing. All variants are in Hardy-Weinberg equilibrium, although the meaning of this equilibrium is unclear. Using this approach, possible associations of sequence variation, and hence of variation in DNA repair, with disease risk can be assessed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".