@article{IR,
      author = {Evans, Nathaniel and Blucher, Aurora and McWeeney,  Shannon},
      url = {http://digitalcollections.ohsu.edu/record/8404},
      title = {Integrating domain knowledge, interpretable deep learning,  and uncertainty quantification for computational prediction  of cell response to drug combinations},
      publisher = {Oregon Health and Science University},
      abstract = {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.},
      number = {IR},
      doi = {https://doi.org/10.6083/08612p16p},
      recid = {8404},
      address = {2020},
}