The Use of DNA Microarrays to Investigate the Pharmacogenomics of Drug Response in Living Systems
Bibliographic record
Abstract
With the advent of DNA microarray analysis, it is now possible to examine the response of virtually the entire human genome to cellular drug exposure and to uncover a wide variety of genes correlating with the establishment of drug resistance. This relatively new field of "pharmacogenomics" is likely to vastly increase our understanding of the mechanisms of drug action and how cells respond and adapt to drug exposure. However, DNA microarray studies typically result in the identification of hundreds of genes that may or may not be of relevance in vivo-particularly when large, genetically diverse study populations are used. The challenge to the researcher is to design experimental systems and approaches which minimize variability in the data, increase the reproducibility amongst experiments, allow array data from multiple experiments to be assessed by a variety of statistical, supervised learning, and data clustering approaches, and provide a clear link between drug response and the expression of specific genes. This review provides a description and critical analysis of recent studies on the pharmacogenomics of drug response and discusses current guidelines and approaches for the performance and analysis of DNA microarray experiments in this area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".