netDx is a patient classifier algorithm that can integrate several types of patient data into a single model. It does this by converting each type of data into a view of patient similarity; i.e. by converting the data into a graph in which more similar patients are tightly linked, while less similar patients are not so tightly linked.
What do you want to do?
- Learn how netDx works
- Get the software (e-mail Gary Bader at the University of Toronto). The code will be made public when the methods paper is published.
- See netDx in action with R vignettes and notebooks.
- Learn to design effective predictors and use netDx with the user manual (in active development ; feedback welcome!)
Reference: Pai S et al. netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology (2019) 15, e8497
Contact Shraddha Pai, shraddha.pai %at% utoronto %dot% ca.