Cancer Immunotherapy Design and Analysis Through Discrete Optimization, Positive-Unlabeled Learning, and Semi-Structured Regression Models
MCML Authors
Emilio Dorigatti
Dr.
* Former Member
Abstract
Emilio Dorigatti
Dr.
* Former Member
Abstract
This thesis advances precision medicine by leveraging artificial intelligence to improve cancer immunotherapy development and tackle key challenges in clinical trials, where high failure rates often stem from insufficient understanding of patient and disease-specific factors. Through novel computational frameworks for cancer vaccine design, methods for handling imbalanced biological data, and hybrid modeling techniques that combine clinical data with imaging, this work demonstrates AI’s potential to personalize and accelerate therapeutic development. These contributions collectively pave the way for more effective, targeted treatments, potentially reducing the time and cost to bring new therapies to market. (Shortened).
BibTeXKey: Dor24