Tool to build a patient classifier using similarity networks

This project is maintained by The Bader Lab, U. Toronto


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.

netDx is now in BioConductor!

Go here to see vignettes (worked examples) and install software.

Methods paper:Pai S, Hui S, Isserlin R, Shah MA, Kaka H, GD Bader (2019) netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology 15, e8497
Software update:Pai S, Weber P, Isserlin R, Kaka H, Hui S, Shah MA, Giudice L, Giugno R, Nøhr AK, Baumbach J, GD Bader netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks F1000Res.9:1239. **netDx is under active development to make the workflow easier to use. The vignettes on the BioConductor page (above) are the latest source of information.

Questions? Contact Shraddha Pai, This page last updated 22 Sep 2021.