Evaluating the effect of pathway incorporation on drug response prediction
Bibliographic record
Abstract
Advances in high-throughput molecular profiling technologies have significantly increased the availability of molecular profiling of cancer cell lines and their responses to various drugs.This has led to rapid development and research in data-driven computational approaches for cancer drug response prediction.Of these approaches, machine learning algorithms have shown great potential in improving drug response prediction, but many viii 5.9 CDS's CCL-Wise and Drug-Wise Performance Comparison Using Different Pathway Databases Under the LCO Validation . . . . . . . . . . . . . . . . .5.10 HiDRA's CCL-Wise and Drug-Wise Performance Comparison Using Different Pathway Databases Under the LCO Validation . . . . . . . . . . . . .5.11 PathDSP's CCL-Wise and Drug-Wise Performance Comparison Using Different Pathway Databases Under the LCO Validation . . . . . . . . . . . . .5.12 Drug-Wise SCC Comparison, Evaluated Under the LDO Validation Scheme 5.13 Drug-Wise RMSE Comparison, Evaluated Under the LDO Validation Scheme ix
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".