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Activity Number: 126 - SPEED: New Methods in Statistical Genomics and Genetics Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306789 Presentation
Title: Likelihood Based Mixture Modeling of Genetic Regulatory Networks
Author(s): David S. Burton* and Matthew N McCall
Companies: University of Rochester Biostatistics and University of Rochester Medical Center
Keywords: network; mixture; cancer; genetics

Gene regulatory networks (GRNs) encode interactions within cells that control the expression levels of genes, and thereby, proteins. GRNs have been shown to play a vital role in both normal cellular function and malignancy. Despite this, there are few statistical methods for estimating GRNs from gene expression measurements. Further, most available methods focus upon delivering a single network estimate by maximizing efficiency in examining the vast space of possible networks. Rather than one estimate, we propose a ternary mixture model with likelihood based scores designed to deliver a posterior distribution of possible networks. Our method is demonstrated on qPCR data from single perturbation experiments conducted in murine cancer cell lines.

Authors who are presenting talks have a * after their name.

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