Algorithms for Survival Analysis of Mutations in Cancer

Deriving clinical utility from large genomic datasets requires the identification of statistically significant associations between genomic measurements and a clinical phenotype. An important instance of this problem is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. I will present some of the algorithmic problems that arise in the identification of mutations associated with survival in cancer.