grab cut

This is the implementation for the following paper:[Learning Sparse Gaussian Graphical Models with Overlapping Blocks](http://papers.nips.cc/paper/6097-learning-sparse-gaussian-graphical-models-with-overlapping-blocks.pdf)‎, Mohammad Javad Hosseini and Su-In Lee. Neural Information Processing Systems (NIPS), 2016.### Running#### SoftwareYou should have R and the R package quic installed on your machine. The file main.py shows an example and will get you started. It uses MILE data (AMLcancer dataset) as input. We have selected 500 genes, consisting of 488 highest varying genes in MILE and 12 genes highly associated with AML:FLT3, NPM1, CEBPA, KIT, N-RAS, MLL, WT1, IDH1/2, TET2, DNMT3A, and ASXL1. Please see data/genes.mat.

grab
.DS_Store
.git
data
GRAB.py
GRAB.pyc
imports.R
main.py
pway_funcs.py
pway_funcs.pyc
quic.py
quic.pyc
README.md