PMED Working Group V: Model Learning of Model Selection

Group Leader:
Heiko Enderling (Moffitt Cancer Center)

John (Neall) Caughman

The aim of this working group is to identify and develop machine learning techniques to identify appropriate model structures to simulate tumor growth and treatment response to maximize model predictive power. In this working group we will align mathematical, statistical and bioinformatics concepts to combine machine learning for parameter identification as well as statistics for model selection to arrive at a model learning platform.

Questions: email

Program on Statistical, Mathematical, and Computational Methods for Precision Medicine (PMED)