Workshops Archive

A Workflow for Error Analysis for Drug Response Prediction via Statistical Standardization and Distribution Analysis

Workshop: WORKS 2025: 20th Workshop on Workflows in Support of Large-Scale Science

Authors: Jake Gwinn (University of Michigan); Justin Wozniak (Argonne National Laboratory (ANL), University of Chicago); and Rajeev Jain, Yitan Zhu, Alex Partin, Thomas Brettin, and Rick Stevens (Argonne National Laboratory (ANL))

Abstract: Drug response prediction is a promising approach to apply machine learning to the development of drugs for a range of cancer types. This method can be used to pre-screen potential drugs, perform high-throughput screening of drug databases, or perform more generalized tasks in machine learning. In an idealized real-world clinical situation, the overall solution must produce a short list of the most promising drugs for a particular patient medical situation. Promising drugs for a given case, however, are very rare, making model performance in this space very difficult. Thus, a great deal of supporting infrastructure must be developed to make this possible, including obtaining and curating datasets, large cross-validation training studies, and post-training inference and analysis. Herein, we describe a new approach for dealing with the rare drug problem, and implement a portable workflow that explores one proposed strategy for addressing it, with results from the exascale supercomputer Aurora.

Back to WORKS 2025: 20th Workshop on Workflows in Support of Large-Scale Science Archive Listing Back to Full Workshop Archive Listing