Bibliographic record
Abstract
Drug sensitivity refers to the differences in the degree of response of different individuals or cells to drugs. Revealing its molecular mechanism is crucial for achieving individualized and precise treatment. However, the average efficacy rate of the anti-cancer drugs approved by the FDA among patients is only about 40%, indicating that the traditional "one-size-fits-all" treatment model is difficult to meet the diverse needs of patients. The development of omics technology has made it possible to conduct a global analysis of biomarkers related to drug responses. By integrating multi-level data such as genomics, transcriptomics, and proteomics, genomic markers closely related to drug sensitivity can be systematically screened out, thereby predicting patients' responses to specific drugs and guiding clinical medication. This study starts from the basic concepts and molecular mechanisms of drug sensitivity, reviews the application of omics data in drug response research, methods and algorithms for genomic marker screening, as well as common data resources, and conducts a case analysis of multi-omics marker screening taking the anti-cancer drug EGFR inhibitor as an example, discussing the current challenges and limitations. Finally, the development direction of precise drug response prediction driven by artificial intelligence is prospected. This study aims to provide a reference for mining drug sensitivity biomarkers using omics data, promoting precision medicine and new drug development.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".