Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Authors: Mohammad Javad Hosseini, Su-In Lee
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 cancer driver genes. |
| Researcher Affiliation | Academia | Mohammad Javad Hosseini1 Su-In Lee1,2 1Department of Computer Science & Engineering, University of Washington, Seattle 2Department of Genome Sciences, University of Washington, Seattle {hosseini, suinlee}@cs.washington.edu |
| Pseudocode | No | No explicit pseudocode or algorithm blocks (e.g., a figure or section labeled 'Pseudocode' or 'Algorithm') were found in the paper. The algorithm steps are described in paragraph text. |
| Open Source Code | Yes | The source code is available at: http://suinlee.cs.washington.edu/software/grab |
| Open Datasets | Yes | We consider the MILE data [20] that measure the m RNA expression levels of 16,853 genes in 541 patients with acute myeloid leukemia (AML). For a better visualization of the network in limited space (Fig 5), we selected 500 genes4, 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. These genes are identified by [21] in a large study on 1,185 patients with AML to be significantly mutated in these AML patients. |
| Dataset Splits | Yes | We generate 100 training samples and 50 validation samples from the multivariate Gaussian distribution with mean zero and the covariance matrix equal to the inverse of the precision matrix. |
| Hardware Specification | Yes | GRAB runs for 0.5-1.5 hours for 500 genes and up to 20 hours for 2,000 genes on a computer with 2.5 GHz Intel Core i5 processor |
| Software Dependencies | No | The paper does not specify versions for any key software components or libraries used for implementation (e.g., 'Python 3.x', 'TensorFlow x.x', 'PyTorch x.x'). |
| Experiment Setup | Yes | For all methods, we considered the regularization parameter λ 2 [.02, .4] with step size .02. Each method selects the regularization parameter using the standard cross-validation (or held out validation) procedure. GRAB selects K based on the validation-set log-likelihood in initialization. In the Z-step of the GRAB learning algorithm, we use step size 1/t, where t is the iteration number and iterate until the relative change in the objective function is less than 10 6 (Section 3.3). We use the warm-start technique between the BCD iterations. For that, we fix the number of blocks to be K = 10 across all methods. |