@article{ETD,
      school = {Ph.D.},
      author = {Evans, Nathaniel J.},
      url = {http://digitalcollections.ohsu.edu/record/43709},
      title = {Mechanistic deep learning for perturbation biology:  application to precision oncology},
      publisher = {Oregon Health and Science University},
      abstract = {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. },
      number = {ETD},
      doi = {https://doi.org/10.6083/bpxhc43709},
      recid = {43709},
      address = {2024-09-19},
}