NetDx: interpretable patient classification using integrated patient similarity networks.

ABSTRACT: Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine-learning approaches across most cancer types. Compared to traditional machine-learning-based patient classif

SUBMITTER: Pai S 

PROVIDER: S-EPMC6423721 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature