PAHdb 2003: What a locus-specific knowledgebase can do
Bibliographic record
Abstract
PAHdb, a legacy of and resource in genetics, is a relational locus-specific database (http://www.pahdb.mcgill.ca). It records and annotates both pathogenic alleles (n = 439, putative disease-causing) and benign alleles (n = 41, putative untranslated polymorphisms) at the human phenylalanine hydroxylase locus (symbol PAH). Human alleles named by nucleotide number (systematic names) and their trivial names receive unique identifier numbers. The annotated gDNA sequence for PAH is typical for mammalian genes. An annotated gDNA sequence is numbered so that cDNA and gDNA sites are interconvertable. A site map for PAHdb leads to a large array of secondary data (attributes): source of the allele (submitter, publication, or population); polymorphic haplotype background; and effect of the allele as predicted by molecular modeling on the phenylalanine hydroxylase enzyme (EC 1.14.16.1) or by in vitro expression analysis. The majority (63%) of the putative pathogenic PAH alleles are point mutations causing missense in translation of which few have a primary effect on PAH enzyme kinetics. Most apparently have a secondary effect on its function through misfolding, aggregation, and intracellular degradation of the protein. Some point mutations create new splice sites. A subset of primary PAH mutations that are tetrahydrobiopterin-responsive is highlighted on a Curators' Page. A clinical module describes the corresponding human clinical disorders (hyperphenylalaninemia [HPA] and phenylketonuria [PKU]), their inheritance, and their treatment. PAHdb contains data on the mouse gene (Pah) and on four orthologous mutant mouse models and their use (for example, in research on oral treatment of PKU with the enzyme phenylalanine ammonia lyase [EC 4.3.1.5]).
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.066 |
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".