GXN

Project information

In this work we introduce Generalizable Gene Self-Expressive Networks, as a new simple, interpretable, and predictive formalism to model gene networks. This package contains two methods, based respectively on ElasticNet and Orthogonal Matching Pursuit regression algorithms, that aim at inferring, assessing and tuning Generalizable Gene Self-Expressive Networks. This package also contains several tutorials that also help to evaluated the generalization capabilities of these new approaches using new internal measure on Three RNAseq datasets from complex eukaryotes, namely C. familiaris, R. norvegicus and H. sapiens.

generalization sparse models