Pharmacogene regulatory elements: from discovery to applications
Bibliographic record
Abstract
Regulatory elements play an important role in the variability of individual responses to drug treatment. This has been established through studies on three classes of elements that regulate RNA and protein abundance: promoters, enhancers and microRNAs. Each of these elements, and genetic variants within them, are being characterized at an exponential pace by next-generation sequencing (NGS) technologies. In this review, we outline examples of how each class of element affects drug response via regulation of drug targets, transporters and enzymes. We also discuss the impact of NGS technologies such as chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq), and the ramifications of new techniques such as high-throughput chromosome capture (Hi-C), chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and massively parallel reporter assays (MPRA). NGS approaches are generating data faster than they can be analyzed, and new methods will be required to prioritize laboratory results before they are ready for the clinic. However, there is no doubt that these approaches will bring about a systems-level understanding of the interplay between genetic variants and drug response. An understanding of the importance of regulatory variants in pharmacogenomics will facilitate the identification of responders versus non-responders, the prevention of adverse effects and the optimization of therapies for individual patients.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".