A Machine Learning Approach to Biology: Applying Data-Driven Model Discovery Methods
Explore a comprehensive lecture on applying data-driven model discovery methods in machine learning biology. Delve into topics such as chimeric antigen receptor T-cell immunotherapy, glioblastoma, and mathematical oncology. Learn about the intersection of mathematical modeling and machine learning, including concepts like Sparse Identification of Nonlinear Dynamics and Kepler's Planetary Motion. Examine case studies on modeling immortalized cell lines, the effects of dexamethasone, and CAR T-cell functional responses. Gain insights into model identification, latent variable identification, and the challenges of higher-order terms in modeling. Discover how these approaches can be applied to various dimensions of biological data, ultimately enhancing our understanding of complex biological systems and their potential applications in cancer research and treatment.