Minisymposium Presentation

The intersection of precision medicine and machine learning (ML) offers a wide range of problems and possible approaches. The prediction of tumor response to single and combination drug agents is an active area of ML application development, as tens of deep learning models are currently available from the community and are under active development. Comparing the behavior of these models is very difficult, and is not a well-studied area, as different projects differ wildly in their problem assumptions and approach to the problem. A range of other problems must also be addressed, including assessing the robustness of the models across a range of health science use cases, hardware resources, and other situations. In this presentation, we will describe our approach to build infrastructure to support the studies outlined above. We are developing a scalable workflow framework to manage, curate, and execute community models in varying scientific problems. Typical use cases include hyperparameter optimization to tune models for general or specific use cases, comparison to study the differences across models, and cross-study analyses that compare training datasets. This presentation will cover the internal design of the system, how it may be extended and used, and results from supercomputers Polaris and Aurora.