Ultranet: efficient solver for the sparse inverse covariance selection problem in gene network modeling.
SUMMARY: Graphical Gaussian Models (GGMs) are a promising approach to identify gene-regulatory networks. Such models can be robustly inferred by solving the sparse inverse covariance selection (SICS) problem. With the high dimensionality of genomics data, fast methods capable of solving large instances of SICS are needed.We developed a novel network modeling tool, Ultranet, that solves the SICS pr
