SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.

ABSTRACT: Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with

SUBMITTER: Huang Z 

PROVIDER: S-EPMC6419526 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature