Learning Interactions in Complex Biological Systems

Daniel Faissol | 17-ERD-036

Executive Summary

Using a novel combination of mechanistic- and data-driven approaches, plus high-performance computing, researchers are developing predictive biological models that will not only enable automated hypothesis generation and experiment prioritization for faster development of countermeasures to human health threats, but also improve predictive capabilities across various National Nuclear Security Administration mission applications.

Publications and Presentations

Petersen, B. K., et al. 2018. "Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine." Journal of Computational Biology. 1-21. LLNL-JRNL-745693.