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ONCO-01
MathOnco Subgroup Mini-Symposium: At the Interface of Modeling and Machine Learning
SMB2025 Follow
Organizers:
Jana Gevertz (The College of New Jersey), Thomas Hillen (University of Alberta), Linh Huynh (Dartmouth College)
Description:
Mathematical oncology models describe cancer dynamics using biologically motivated equations that are validated using experimental data. Machine learning models, on the other hand, leverage vast amounts of data to make predictions without necessarily including any a-priori biological knowledge. Mathematical models result in biologically interpretable predictions, whereas machine learning models excel at handling complex, high-dimensional datasets. Thus, work at the interface of modeling and machine learning holds the promise of realizing the advantages of both methods. In this mini-symposium, we will showcase how cancer research benefits from a combined approach of mathematical modeling and machine learning.
Diversity Statement:
We did initially invite an equal split of men and women to speak. With the many people who turned us down, our gender balance is not ideal (1 woman, 7 men). We did work hard to also balance early, mid and later career researchers, and were successful in that regard.
Thomas Yankeelov (University of Texas at Austin)
"Integrating mechanism-based and data-driven modeling to predict treatment response in cancer"
Adam Maclean (University of Southern California)
"Dynamic rewiring of cell-cell interaction networks in metastatic TMEs to empower checkpoint inhibitors"
Paul Macklin (Indiana University Bloomington)
"Integrating high-throughput exploration and learning with agent-based models of cancer"
Venkata Manem (Université Laval)
"Leveraging Bioinformatics and Machine Learning for the Discovery of Cancer Biomarkers"
Kit Gallagher (University of Oxford)
"redicting Treatment Outcomes from Adaptive Therapy - A New Mathematical Biomarker"
John Metzcar (University of Minnesota)
"Mechanistic learning to understand tumor-immune dynamics and optimize therapies"
Lucas Gillewater (University of Colorado Denver)
"Computational methods for subtyping through biologically informed integration of multi-omic data"
Lena Podina (University of Waterloo)
"Universal Physics-Informed Neural Networks and their Applications"