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September 2014 Joint estimation of multiple related biological networks
Chris J. Oates, Jim Korkola, Joe W. Gray, Sach Mukherjee
Ann. Appl. Stat. 8(3): 1892-1919 (September 2014). DOI: 10.1214/14-AOAS761


Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical units whose underlying networks may differ but are likely to share features. Here we present a hierarchical Bayesian formulation for joint estimation of multiple networks in this nonidentically distributed setting. The approach is general: given a suitable class of graphical models, it uses an exchangeability assumption on networks to provide a corresponding joint formulation. Motivated by emerging experimental designs in molecular biology, we focus on time-course data with interventions, using dynamic Bayesian networks as the graphical models. We introduce a computationally efficient, deterministic algorithm for exact joint inference in this setting. We provide an upper bound on the gains that joint estimation offers relative to separate estimation for each network and empirical results that support and extend the theory, including an extensive simulation study and an application to proteomic data from human cancer cell lines. Finally, we describe approximations that are still more computationally efficient than the exact algorithm and that also demonstrate good empirical performance.


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Chris J. Oates. Jim Korkola. Joe W. Gray. Sach Mukherjee. "Joint estimation of multiple related biological networks." Ann. Appl. Stat. 8 (3) 1892 - 1919, September 2014.


Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1304.62136
MathSciNet: MR3271357
Digital Object Identifier: 10.1214/14-AOAS761

Keywords: Bayesian network , belief propagation , hierarchical model , information sharing

Rights: Copyright © 2014 Institute of Mathematical Statistics


Vol.8 • No. 3 • September 2014
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