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Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Mohammad Javad Hosseini · Su-In Lee

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #144

We present a novel framework, called GRAB (GRaphical models with overlApping Blocks), to capture densely connected components in a network estimate. GRAB takes as input a data matrix of p variables and n samples, and jointly learns both a network among p variables and densely connected groups of variables (called `blocks'). GRAB has four major novelties as compared to existing network estimation methods: 1) It does not require the blocks to be given a priori. 2) Blocks can overlap. 3) It can jointly learn a network structure and overlapping blocks. 4) It solves a joint optimization problem with the block coordinate descent method that is convex in each step. We show that GRAB reveals the underlying network structure substantially better than four state-of-the-art competitors on synthetic data. When applied to cancer gene expression data, GRAB outperforms its competitors in revealing known functional gene sets and potentially novel genes that drive cancer.

Author Information

Mohammad Javad Hosseini (University of Washington)
Su-In Lee (University of Washington)

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