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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: Methods and Applications for Mixed Graphical Models

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"Mixed Data'' comprising a large number of heterogeneous variables (e.g. count, binary, continuous, skewed continuous, among others) is prevalent in varied areas such as imaging genetics, national security, social networking, Internet advertising, and our particular motivation - high-throughput integrative genomics. There have been limited efforts at statistically modeling such mixed data jointly. Recently, new Mixed Markov Random Field (MRFs) distributions, or graphical models, were proposed that assume each node-conditional distribution arises from a different exponential family model. These yield joint densities, which can directly parameterize dependencies over mixed variables. Fitting these models to perform mixed graph selection entails estimating penalized generalized linear models with mixed covariates. This task, however, poses many challenges due to differences in the scale and potential signal interference between mixed covariates. In this poster, we introduce this novel class of MRFs, study model estimation challenges theoretically and empirically, and propose a new iterative block estimation strategy. Our methods are applied to infer a gene regulatory network that integrates methylation, small RNA expression, and gene expression data to fully understand regulatory relationships in ovarian cancer.

avatar for Genevera Allen

Genevera Allen

Assistant Professor of Statistics and Electrical and Computer Engineering, Rice University
Genevera Allen is the Dobelman Family Junior Chair and an Assistant Professor of Statistics and Electrical and Computer Engineering at Rice University. She is also a member of the Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital and Baylor College... Read More →

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)