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Two-way sparsity for time-varying networks with applications in genomics. (English) Zbl 1478.62320

Summary: We propose a novel way of modelling time-varying networks by inducing two-way sparsity on local models of node connectivity. This two-way sparsity separately promotes sparsity across time and sparsity across variables (within time). Separation of these two types of sparsity is achieved through a novel prior structure which draws on ideas from the Bayesian lasso and from copula modelling. We provide an efficient implementation of the proposed model via a Gibbs sampler, and we apply the model to data from neural development. In doing so, we demonstrate that the proposed model is able to identify changes in genomic network structure that match current biological knowledge. Such changes in genomic network structure can then be used by neurobiologists to identify potential targets for further experimental investigation.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference
62H22 Probabilistic graphical models
92D20 Protein sequences, DNA sequences

Software:

SCENIC; SCODE; glasso; CODA; ZIFA
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Full Text: DOI arXiv

References:

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