000008404 001__ 8404
000008404 005__ 20240410110855.0
000008404 0247_ $$2DOI$$a10.6083/08612p16p
000008404 037__ $$aIR
000008404 041__ $$aeng
000008404 245__ $$aIntegrating domain knowledge, interpretable deep learning, and uncertainty quantification for computational prediction of cell response to drug combinations
000008404 260__ $$bOregon Health and Science University
000008404 269__ $$a2020
000008404 336__ $$aAbstract
000008404 520__ $$aA major challenge in precision oncology is accurately modeling the molecular machinery that governs cellular sensitivity or resistance to a given drug or drug combination. While predicting drug response is an active area, model performance and interpretability vary depending on the method and prediction task, and leaves room for improvement. In this talk, I'll discuss how these methods may be combined in a biology-centric framework that provides robust drug-response prediction, an interpretable latent space, and uncertainty quantification.
000008404 540__ $$fCC BY
000008404 542__ $$fIn copyright - joint owners
000008404 650__ $$aDeep Learning$$012734
000008404 650__ $$aMachine Learning$$011449
000008404 6531_ $$aprecision oncology
000008404 6531_ $$adrug response
000008404 6531_ $$agraphical models
000008404 6531_ $$agraph convolutional networks
000008404 6531_ $$avisible neural networks
000008404 6531_ $$auncertainty quantification
000008404 6531_ $$abayesian modeling
000008404 691__ $$aSchool of Medicine$$041369
000008404 692__ $$aDepartment of Medical Informatics and Clinical Epidemiology$$041422
000008404 7001_ $$aEvans, Nathaniel$$uOregon Health and Science University$$041354
000008404 7001_ $$aBlucher, Aurora$$uOregon Health and Science University$$041354
000008404 7001_ $$aMcWeeney, Shannon$$uOregon Health and Science University$$041354
000008404 711__ $$aResearch Week$$uOregon Health and Science University$$d2020
000008404 8564_ $$95af3b9ba-c5e8-44e9-bf51-9193f000e8a4$$s47588$$uhttps://digitalcollections.ohsu.edu/record/8404/files/ResearchWeek.2020.Evans.Nathaniel.pdf
000008404 905__ $$a/rest/prod/08/61/2p/16/08612p16p
000008404 980__ $$aResearch Week