Polymorphisms of human nuclear receptors that control expression of drug-metabolizing enzymes
Bibliographic record
Abstract
Phenotypic variation in human drug metabolism frequently can be attributed to polymorphisms in genes that encode drug-metabolizing enzymes (DMEs). However, levels of Phase I and Phase II DMEs also vary because many of these enzymes are induced by a myriad of xenobiotic chemicals. Individual differences in the capacity for induction contribute to variation in drug metabolism in human populations. Induction is mediated by intracellular receptors that act as ligand-dependent transcription factors, including several members of the nuclear receptor (NR) superfamily and the aryl hydrocarbon receptor (AHR). Genetic variations (SNPs and others) exist in genes that encode these human receptors but few of the known polymorphisms have any significant effect on enzyme induction. We suggest that the current scarcity of SNPs that are able to alter function in the DME-regulating NRs reflects considerable evolutionary selective pressures that conserve the key functional domains in these receptors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".