Genomics and Biomarker Research in Drug Development: Overrated, or a Revolution to Come?
Bibliographic record
Abstract
At the basic science level, genomics and biomarker research have contributed great strides to our collective scientific knowledge and understanding of biological processes. Some of these research techniques have been applied to the field of drug development, permitting the new branch of pharmacogenomics to take shape. Although such research has promised to usher in an era of personalized medicine by providing unique treatments targeted to specific patient subgroups, progress in the field has been relatively slow thus far. To live up to its potential, researchers in the field must properly understand the inherent limitations of biomarkers, governmental regulations must adapt to changing technologies, and research must target clinically meaningful patient outcomes to make positive contributions to patient care.
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.
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 | 0.115 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.015 | 0.055 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 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".