Multi-SConES

Mahito Sugiyama, Chloe-Agathe Azencott, Dominik Grimm, Yoshinobu Kawahara and Karsten Borgwardt

Multi-Task Feature Selection on Multiple Networks via Maximum Flows

Summary

This is a multi-task version of SConES, that allows for network GWAS with more than one network and more than one phenotype. This method achieves multi-task feature selection coupled with multiple network regularizers using a maximum-flow algorithm.

Code

An R implementation of the method can be found in our GitHub repository external pagehere.

Publication

Please see the following paper for detailed information and refer it in your published research.

Multi-Task Feature Selection on Multiple Networks via Maximum Flows

Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara and Karsten Borgwardt
Proceedings of the 2014 SIAM International Conference on Data Mining 2014, 199-207
external pageOnline | ETH Research Collection | Project page | external pageGitHub | Supplement

Multi-SConES

Multi-Task Feature Selection on Multiple Networks via Maximum Flows

Summary

Code

Publication

Multi-Task Feature Selection on Multiple Networks via Maximum Flows

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