Classification of missense substitutions in the BRCA genes: A database dedicated to Ex-UVs
Bibliographic record
Abstract
Unclassified sequence variants (UVs) arising from clinical mutation screening of cancer susceptibility genes present a frustrating issue to clinical genetics services and the patients that they serve. We created an open-access database holding missense substitutions from the breast and ovarian cancer susceptibility genes BRCA1 and BRCA2. The main inclusion criterion is that each variant should have been assessed in a published work that used the Bayesian integrated evaluation of unclassified BRCA gene variants. Transfer of data on these substitutions from the original publications to our database afforded an opportunity to analyze the missense substitutions under a single model and to remove inconsistencies that arose during the evolution of the integrated evaluation over the last decade. This analysis also afforded the opportunity to reclassify these missense substitutions according to the recently published IARC 5-Class system. From an initial set of 248 missense substitutions, 31 were set aside due to nonnegligible probability to interfere with splicing. Of the remaining substitutions, 28 fell into one of the two pathogenic classes (IARC Class 4 or 5), 174 fell into one of the two nonpathogenic classes (IARC Class 1 or 2), and 15 remain in IARC Class 3, "Uncertain." The database is available at http://brca.iarc.fr/LOVD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".