title: Structured Learning of Gaussian Graphical Models.
abstract: We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions. We assume that most aspects of the networks are shared, but that there are some structured differences between them. Specifically, the network differences are generated fromnode perturbations: a few nodes are perturbed across networks, and most or all edges stemming from such nodes differ between networks. This corresponds to a simple model for the mechanism underlying many cancers, in which the gene regulatory network is disrupted due to the aberrant activity of a few specific genes. We propose to solve this problem using theperturbed-node joint graphical lasso, a convex optimization problem that is based upon the use of arow-column overlap normpenalty. We then solve the convex problem using an alternating directions method of multipliers algorithm. Our proposal is illustrated on synthetic data and on an application to brain cancer gene expression data.
authors.affiliation: Electrical Engineering, Univ. of Washington.
authors.forename: Karthik
authors.initials: K
authors.lastname: Mohan
authors.affiliation: Computer Science and Engineering, Univ. of Washington.
authors.forename: Michael Jae-Yoon
authors.initials: MJ
authors.lastname: Chung
authors.affiliation: Computer Science and Engineering, Univ. of Washington.
authors.forename: Seungyeop
authors.initials: S
authors.lastname: Han
authors.affiliation: Biostatistics, Univ. of Washington.
authors.forename: Daniela
authors.initials: D
authors.lastname: Witten
authors.affiliation: Computer Science and Engineering, and Genome Sciences, Univ. of Washington.
authors.forename: Su-In
authors.initials: SI
authors.lastname: Lee
authors.affiliation: Electrical Engineering, Univ. of Washington.
authors.forename: Maryam
authors.initials: M
authors.lastname: Fazel