Integrating domain knowledge, interpretable deep learning, and uncertainty quantification for computational prediction of cell response to drug combinations
A major challenge in precision oncology is accurately modeling the molecular machinery that governs cellular sensitivity or resistance to a given drug or drug combination. While predicting drug response is an active area, model performance and interpretability vary depending on the method and prediction task, and leaves room for improvement. In this talk, I'll discuss how these methods may be combined in a biology-centric framework that provides robust drug-response prediction, an interpretable latent space, and uncertainty quantification.
Details
Title
Integrating domain knowledge, interpretable deep learning, and uncertainty quantification for computational prediction of cell response to drug combinations
Creator
Evans, Nathaniel : Oregon Health and Science University Blucher, Aurora : Oregon Health and Science University McWeeney, Shannon : Oregon Health and Science University
Meeting Name
Research Week, Oregon Health and Science University, 2020
