TY  - GEN
AB  - The work presented in this dissertation focuses on the development of robust deep learning models to predict in-vitro cancer drug responses, with utility in precision oncology research tasks such as drug repurposing and prioritization of disease-specific drug combinations. This work addresses two critical shortcomings in the current approaches to predicting cancer drug responses with deep learning: 1) data quality issues inherent in high-throughput drug screening datasets by developing algorithms for detection of atypical or low-quality data and 2) improved prediction and utility of perturbation biology models by developing algorithms that operate on mechanistic prior knowledge. 
AD  - Oregon Health and Science University
AU  - Evans, Nathaniel J.
DA  - 2024-09-19
DO  - 10.6083/bpxhc43709
DO  - doi
ED  - McWeeney, Shannon
ED  - Song, Xubo 
ED  - Wu, Guanming 
ED  - Mills, Gordon B. 
ED  - Mooney, Michael
ED  - Academic advisor
ED  - Chair
ED  - Advisor
ED  - Advisor
ED  - Advisor
ID  - 43709
KW  - Deep Learning
KW  - Biomarkers, Pharmacological
KW  - Precision Medicine
KW  - graph learning
KW  - precision oncology
KW  - drug repurposing
KW  - perturbation biology
KW  - mechanistic modeling 
L1  - https://digitalcollections.ohsu.edu/record/43709/files/Evans.Nathaniel.2024.pdf
L2  - https://digitalcollections.ohsu.edu/record/43709/files/Evans.Nathaniel.2024.pdf
L4  - https://digitalcollections.ohsu.edu/record/43709/files/Evans.Nathaniel.2024.pdf
LA  - eng
LK  - https://digitalcollections.ohsu.edu/record/43709/files/Evans.Nathaniel.2024.pdf
N2  - The work presented in this dissertation focuses on the development of robust deep learning models to predict in-vitro cancer drug responses, with utility in precision oncology research tasks such as drug repurposing and prioritization of disease-specific drug combinations. This work addresses two critical shortcomings in the current approaches to predicting cancer drug responses with deep learning: 1) data quality issues inherent in high-throughput drug screening datasets by developing algorithms for detection of atypical or low-quality data and 2) improved prediction and utility of perturbation biology models by developing algorithms that operate on mechanistic prior knowledge. 
PB  - Oregon Health and Science University
PY  - 2024-09-19
T1  - Mechanistic deep learning for perturbation biology: application to precision oncology
TI  - Mechanistic deep learning for perturbation biology: application to precision oncology
UR  - https://digitalcollections.ohsu.edu/record/43709/files/Evans.Nathaniel.2024.pdf
Y1  - 2024-09-19
ER  -