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Classroom Contents
A Machine Learning Approach to Biology: Applying Data-Driven Model Discovery Methods
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- 1 Intro
- 2 Motivation
- 3 Chimeric antigen receptor Tcell immunotherapy
- 4 Glioblastoma
- 5 City of Hope
- 6 Clinical trials
- 7 Mathematical oncology
- 8 Immunotherapy
- 9 Chicken and the Egg
- 10 Mathematical modeling is an art
- 11 How modeling ends up going
- 12 How does mechanistic modeling
- 13 How does modeling and machine learning come together
- 14 What if you didnt have to do that
- 15 Sparse Identification Nonlinear Dynamics
- 16 Keplers Planetary Motion
- 17 What comes first
- 18 First paper
- 19 Modeling immortalized cell lines
- 20 What happens with dexamethasone
- 21 Dynamic phase faces
- 22 Rule change
- 23 Best model
- 24 Dolly
- 25 Model Identification
- 26 Tokens Theorem
- 27 Shadow manifolds
- 28 Latent variable identification
- 29 Library
- 30 Growth Depth
- 31 Car T Cell Functional Responses
- 32 Car T Cell Binding Dynamics
- 33 Minimal Accuracy
- 34 Models
- 35 Dual fit
- 36 Higher order terms
- 37 Data in code
- 38 Two Dimensions
- 39 Summary
- 40 Final Thought
- 41 Conclusion
