TY  - GEN
AB  - Cancer 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.
AU  - Lazar, Nathan H.
DA  - 2017
DO  - 10.6083/m40k2833
DO  - DOI
ID  - 7596
KW  - Computational Biology
KW  - Cell Line
KW  - Machine Learning
KW  - Cellular Microenvironment
KW  - cancer
L1  - https://digitalcollections.ohsu.edu/record/7596/files/Lazar.Nathan.2017.pdf
L2  - https://digitalcollections.ohsu.edu/record/7596/files/Lazar.Nathan.2017.pdf
L4  - https://digitalcollections.ohsu.edu/record/7596/files/Lazar.Nathan.2017.pdf
LK  - https://digitalcollections.ohsu.edu/record/7596/files/Lazar.Nathan.2017.pdf
N2  - Cancer 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.
PB  - Oregon Health and Science University
PY  - 2017
T1  - A bayesian tensor factorization algorithm to predict drug response in cancer cell lines
TI  - A bayesian tensor factorization algorithm to predict drug response in cancer cell lines
UR  - https://digitalcollections.ohsu.edu/record/7596/files/Lazar.Nathan.2017.pdf
Y1  - 2017
ER  -