TY  - GEN
N2  - 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.
DO  - 10.6083/08612p16p
DO  - DOI
AB  - 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.
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
AD  - Oregon Health and Science University
T1  - Integrating domain knowledge, interpretable deep learning, and uncertainty quantification for computational prediction of cell response to drug combinations
DA  - 2020
AU  - Evans, Nathaniel
AU  - Blucher, Aurora
AU  - McWeeney, Shannon
L1  - https://digitalcollections.ohsu.edu/record/8404/files/ResearchWeek.2020.Evans.Nathaniel.pdf
PB  - Oregon Health and Science University
LA  - eng
PY  - 2020
ID  - 8404
L4  - https://digitalcollections.ohsu.edu/record/8404/files/ResearchWeek.2020.Evans.Nathaniel.pdf
KW  - Deep Learning
KW  - Machine Learning
KW  - precision oncology
KW  - drug response
KW  - graphical models
KW  - graph convolutional networks
KW  - visible neural networks
KW  - uncertainty quantification
KW  - bayesian modeling
TI  - Integrating domain knowledge, interpretable deep learning, and uncertainty quantification for computational prediction of cell response to drug combinations
Y1  - 2020
L2  - https://digitalcollections.ohsu.edu/record/8404/files/ResearchWeek.2020.Evans.Nathaniel.pdf
LK  - https://digitalcollections.ohsu.edu/record/8404/files/ResearchWeek.2020.Evans.Nathaniel.pdf
UR  - https://digitalcollections.ohsu.edu/record/8404/files/ResearchWeek.2020.Evans.Nathaniel.pdf
ER  -