Bernoulli Institute, Department of Mathematics, Faculty of Science and Engineering, Groningen University, Groningen 9747 AG, The Netherlands.
Bioinformatics. 2020 Feb 15;36(4):1198-1207. doi: 10.1093/bioinformatics/btz690.
Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular tool for learning networks with time-varying interaction parameters. A multiple changepoint process is used to divide the data into disjoint segments and the network interaction parameters are assumed to be segment-specific. The objective is to infer the network structure along with the segmentation and the segment-specific parameters from the data. The conventional (uncoupled) NH-DBNs do not allow for information exchange among segments, and the interaction parameters have to be learned separately for each segment. More advanced coupled NH-DBN models allow the interaction parameters to vary but enforce them to stay similar over time. As the enforced similarity of the network parameters can have counter-productive effects, we propose a new consensus NH-DBN model that combines features of the uncoupled and the coupled NH-DBN. The new model infers for each individual edge whether its interaction parameter stays similar over time (and should be coupled) or if it changes from segment to segment (and should stay uncoupled).
Our new model yields higher network reconstruction accuracies than state-of-the-art models for synthetic and yeast network data. For gene expression data from A.thaliana our new model infers a plausible network topology and yields hypotheses about the light-dependencies of the gene interactions.
Data are available from earlier publications. Matlab code is available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
非齐次动态贝叶斯网络（NH-DBNs）是一种用于学习具有时变交互参数的网络的流行工具。使用多个变点过程将数据分为不相交的段，并且假设网络交互参数是特定于段的。目标是从数据中推断网络结构以及分段和分段特定参数。传统的（非耦合）NH-DBN 不允许在段之间交换信息，并且必须为每个段分别学习交互参数。更先进的耦合 NH-DBN 模型允许交互参数变化，但强制它们随时间保持相似。由于网络参数的强制相似可能会产生适得其反的效果，因此我们提出了一种新的共识 NH-DBN 模型，该模型结合了非耦合和耦合 NH-DBN 的特点。新模型推断每个单独的边，其交互参数是否随时间保持相似（应该耦合），或者它是否从段到段变化（应该保持非耦合）。
我们的新模型在合成和酵母网络数据方面产生了比最先进模型更高的网络重建精度。对于来自 A.thaliana 的基因表达数据，我们的新模型推断出了一个合理的网络拓扑结构，并提出了关于基因相互作用对光的依赖性的假设。
数据可从早期出版物获得。Matlab 代码可在 Bioinformatics 在线获得。
补充数据可在 Bioinformatics 在线获得。
