References
1. 
Pai, S. et al. netDx: Interpretable patient classification using integrated patient similarity networks. Mol. Syst. Biol. 15, e8497 (2019).
2. 
Pai, S. et al. netDx: Software for building interpretable patient classifiers by multi-’omic data integration using patient similarity networks. F1000Research 9, 1239 (2021).
3. 
Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
4. 
Yersal, O. & Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 5, 412–424 (2014).
5. 
Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment map: A network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984 (2010).
6. 
Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545–15550 (2005).
7. 
Gustavsen, J. A., Pai, S., Isserlin, R., Demchak, B. & Pico, A. R. RCy3: Network biology using cytoscape from within R. F1000Res. 8, 1774 (2019).
8. 
Kucera, M., Isserlin, R., Arkhangorodsky, A. & Bader, G. D. AutoAnnotate: A cytoscape app for summarizing networks with semantic annotations. F1000Res. 5, 1717 (2016).