A Machine Learning Approach to Biology: Applying Data-Driven Model Discovery Methods

A Machine Learning Approach to Biology: Applying Data-Driven Model Discovery Methods

Mathematical Oncology via YouTube Direct link

Conclusion

41 of 41

41 of 41

Conclusion

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Classroom Contents

A Machine Learning Approach to Biology: Applying Data-Driven Model Discovery Methods

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

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