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Minisymposium Presentation
Towards an Open Ecosystem for FAIR (Findable, Accessible, Interoperable, Reusable) Drug Response Prediction Models and Data
Presenter
Dr. Eric Stahlberg directs cancer data science initiatives at the Frederick National Laboratory. He has been instrumental in establishing the Frederick National Laboratory’s HPC initiative and in assembling collaborative teams across multiple, complex organizations to advance predictive oncology. Stahlberg has played a leadership role in many key partnerships, including forming the collaboration between the National Cancer Institute and the Department of Energy where the agencies are accelerating progress in precision oncology and computing. He has led program efforts establishing foundations for digital twin applications in cancer, and now advancing personalized medicine for all individuals through virtual human models and digital twin approaches. He co-organizes the annual Computational Approaches for Cancer at SC and HPC Applications of Precision Medicine workshops at ISC. Most recently, he spearheaded the first Virtual Human Global Summit in October 2023.
Description
Fueled by pervasive supercomputing and growing availability of response data have led to a dramatic increase in the availability of predictive drug response models. Over the past several years, the collaboration between the US Department of Energy and the US National Cancer Institute have resulted in multiple advances towards an Open and FAIR ecosystem for creating, evaluating and sharing drug response models. The presentation will provide an overview of several of these projects providing publicly available resources including IMPROVE (Innovative Methods and data for Predictive Oncology Validation and Evaluation), ATOM (Accelerating Therapeutics for Opportunities in Medicine) and MODAC (the NCI Predictive Oncology Model and Data Clearinghouse). Future directions and priorities will also be shared in terms of ethical AI and guidance for transparency to build trust and reuse of trained models.