TWO-WAY SPARSITY FOR TIME-VARYING NETWORKS WITH APPLICATIONS IN GENOMICS
TWO-WAY SPARSITY FOR TIME-VARYING NETWORKS WITH APPLICATIONS IN GENOMICS
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DOI：
10.1214/20-aoas1416
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发表时间：
2021-06-01
影响因子：
1.8
通讯作者：
Silva, Ricardo
中科院分区：
文献类型：
--
作者：
Bartlett, Thomas E.;Kosmidis, Ioannis;Silva, Ricardo
我们提出了一种新的建模时变网络的方法，即在节点连接的局部模型上引入双向稀疏性。这种双向稀疏性分别促进了跨时间的稀疏性和跨变量（时间内）的稀疏性。这两种稀疏性的分离是通过一种新的先验结构实现的，该结构借鉴了贝叶斯套索和copula建模的思想。我们通过Gibbs采样器提供了所提出模型的有效实现，并将该模型应用于神经发育数据。在这样做的过程中，我们证明了所提出的模型能够识别与当前生物学知识相匹配的基因组网络结构的变化。基因组网络结构的这种变化可以被神经生物学家用来确定进一步实验研究的潜在目标。
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.