Check out our paper on Nature Communications!
Focus on Innovating Your Models — DrEval Handles the Rest!
- DrEval is a toolkit that ensures drug response prediction evaluations are statistically sound, biologically meaningful, and reproducible.
- Focus on model innovation while using our automated standardized evaluation protocols and preprocessing workflows.
- A flexible model interface supports all model types (e.g. machine learning, statistical models, network-based analyses).
Use DrEval to build drug response models that have an impact
- Maintained, up-to-date baseline catalog, no need to re-implement literature models
- Gold standard datasets for benchmarking
- Consistent application-driven evaluation
- Ablation studies with permutation tests
- Cross-study evaluation for generalization analysis
- Optimized nextflow pipeline for fast experiments
- Easy-to-use hyperparameter tuning
- Paper-ready visualizations to display performance
This project is a collaboration of the Technical University of Munich (TUM, Germany) and the Freie Universität Berlin (FU, Germany).
In the critical difference diagram, you can see which models outperform which other models significantly. The diagram is based on the average ranks of the models across all datasets and metrics, and the critical difference is calculated using the Nemenyi test. If model A is outside the bar corresponding to model B, it is significantly better (if it lies to the left) or worse (if it lies to the right) than model B.
