Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose–response experiments
Bibliographic record
Abstract
MOTIVATION: The efficacy of a chemical compound is often tested through dose-response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt-Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose-response experiments. RESULTS: Our results well demonstrate the informativeness gain of our Bayesian approach in comparison to the commonly used Marquardt-Levenberg algorithm. It is capable to characterize the noise of dataset while inferring probable values distributions for the efficacy metrics. It can also evaluate the difference between the metrics of two datasets and compute the probability that one value is greater than the other. The conclusions that can be drawn from such analyzes are more precise. AVAILABILITY AND IMPLEMENTATION: We implemented a simple web interface that allows the users to analyze a single dose-response dataset, as well as to statistically compare the metrics of two datasets.
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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 | 0.056 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".