The Use of DNA Microarrays to Investigate the Pharmacogenomics of Drug Response in Living Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it