Pascal Iversen
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pascivers.bsky.social
Pascal Iversen
@pascivers.bsky.social
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judith-bernett.bsky.social
New pipeline version finally out now! 🥳
July 3, 2025 at 1:52 PM
pascivers.bsky.social
Despite hundreds of published models for cancer drug response prediction, none are used in clinical practice.

Why?

In our preprint, we outline key challenges and introduce a living benchmark to help the field move forward.

Check out the thread by my co–first author @judith-bernett.bsky.social:
judith-bernett.bsky.social Judith Bernett @judith-bernett.bsky.social · Jun 3
🧬🖥️So excited to show you the outcome of @pascivers.bsky.social and my latest project: "From Hype to Health Check: Critical Evaluation of Drug Response Prediction Models with DrEval" doi.org/10.1101/2025.05.26.655288, published with M. Picciani, M. Wilhelm, K. Baum & @itisalist.bsky.social.
🧵1/10
Overview of the DrEval framework. Via input options, implemented state-of-the-art models can be compared against baselines of varying complexity. We address obstacles to progress in the field at each point in our pipeline: Our framework is available on PyPI and nf-core and we follow FAIReR standards for optimal reproducibility. DrEval is easily extendable as demonstrated here with a pseudocode implementation of a proteomics-based random forest. Custom viability data can be preprocessed with CurveCurator, leading to more consistent data and metrics. DrEval supports five widely used datasets with application-aware train/test splits that enable detecting weak generalization. Models are free to use provided or custom cell line– and drug features. The pipeline supports randomization-based ablation studies and performs robust hyperparameter tuning for all models. Evaluation is conducted using meaningful, bias-resistant metrics to avoid inflated results from artifacts such as Simpson’s paradox. All results are compiled into an interactive HTML report. Created in https://BioRender.com.
June 3, 2025 at 2:58 PM