The androgen receptor gene mutations database: 2012 update
Bibliographic record
Abstract
The current version of the androgen receptor gene (AR) mutations database is described. A major change to the database is that the nomenclature and numbering scheme now conforms to all Human Genome Variation Society norms. The total number of reported mutations has risen from 605 to 1,029 since 2004. The database now contains a number of mutations that are associated with prostate cancer (CaP) treatment regimens, while the number of AR mutations found in CaP tissues has more than doubled from 76 to 159. In addition, in a number of androgen insensitivity syndrome (AIS) and CaP cases, multiple mutations have been found within the same tissue samples. For the first time, we report on a disconnect within the AIS phenotype-genotype relationship among our own patient database, in that over 40% of our patients with a classic complete AIS or partial AIS phenotypes did not appear to have a mutation in their AR gene. The implications of this phenomenon on future locus-specific mutation database (LSDB) development are discussed, together with the concept that mutations can be associated with both loss- and gain-of-function, and the effect of multiple AR mutations within individuals. The database is available on the internet (http://androgendb.mcgill.ca), and a web-based LSDB with the variants using the Leiden Open Variation Database platform is available at http://www.lovd.nl/AR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".