Joint estimation of multiple networks from time course data

By Chris J. Oates, James Korkola, Joe W. Gray, Sach Mukherjee. 2013

20 citations recorded by the source corpus.

Graphical models are widely used to make inferences concerning interplay in multivariate systems, as modelled by a conditional independence graph or network. In many applications, data are collected from multiple individuals whose networks may differ but are likely to share many features. Here we present a hierarchical Bayesian formulation for joint estimation of such networks. The formulation is general and can be applied to a number of specific graphical models. Motivated by applications in biology, we focus on time-course data with interventions and introduce a computationally efficient, deterministic algorithm for exact inference in this setting. Application of the proposed method to simulated data demonstrates that joint estimation can improve ability to infer individual networks as well as differences between them. Finally, we describe approximations which are still more computationally efficient than the exact algorithm and demonstrate good empirical performance.

Subfields: Molecular Biology

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In Molecular Biology

1. Sara Magliacane
2. Tom Claassen
3. Joris M. Mooij

Citations in the corpus

• Joint Causal Inference from Multiple Contexts (2016, background): “structure learning" (i.e., Bayesian network learning) approaches that put a prior on the similarity of the DAGs in multiple contexts which encourages them to be similar have been proposed (e.g., Oates et al., 2014, 2016b). Some methods which allow for a single context variable have been applied”