RT Journal Article
SR Electronic
T1 netDx: Interpretable patient classification using integrated patient similarity networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 084418
DO 10.1101/084418
A1 Pai, Shraddha
A1 Hui, Shirley
A1 Isserlin, Ruth
A1 Shah, Muhammad A
A1 Kaka, Hussam
A1 Bader, Gary D
YR 2018
UL http://biorxiv.org/content/early/2018/05/25/084418.abstract
AB 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 easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. As a machine learning method, netDx demonstrates consistently excellent performance in a cancer survival benchmark across four cancer types by integrating up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types and its best model outperforms all other methods for two cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a software complete implementation of netDx along with sample files and automation workflows in R.