000007596 001__ 7596
000007596 005__ 20240124114246.0
000007596 0247_ $$2DOI$$a10.6083/m40k2833
000007596 037__ $$aETD
000007596 245__ $$aA bayesian tensor factorization algorithm to predict drug response in cancer cell lines
000007596 260__ $$bOregon Health and Science University
000007596 269__ $$a2017
000007596 336__ $$aDissertation
000007596 502__ $$bPh.D.
000007596 520__ $$aCancer is now the biggest single cause of mortality worldwide, incidence has increased by 11% in four years and cases are forecast to rise by 75% over the next 20 years [1]. Despite the huge amount of resources devoted to combating this terrible disease, progress has been slow. Most patients are still treated with painful blunt instruments like radiation and chemotherapy while new targeted treatments addressing specific genetic causes often only help a small portion of the population. Why haven't scientists and doctors been able to do more? Because each patient is different, each cancer is unique and we lack the framework for collecting, processing and understanding the mountain of information that we can generate on cancers, treatments and responses.
000007596 542__ $$fIn copyright - single owner
000007596 650__ $$aComputational Biology$$031511
000007596 650__ $$aCell Line$$016284
000007596 650__ $$aMachine Learning$$011449
000007596 650__ $$aCellular Microenvironment$$039821
000007596 6531_ $$acancer
000007596 691__ $$aSchool of Medicine$$041369
000007596 7001_ $$aLazar, Nathan H.
000007596 8564_ $$950c945da-efca-469a-8779-50c2fb2b9a34$$s9966977$$uhttps://digitalcollections.ohsu.edu/record/7596/files/Lazar.Nathan.2017.pdf
000007596 905__ $$a/rest/prod/jd/47/2x/01/jd472x01z
000007596 909CO $$ooai:digitalcollections.ohsu.edu:7596$$pstudent-work
000007596 980__ $$aTheses and Dissertations