Abstract
MotivationNon-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).ResultsOur 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.Availability and implementationData are available from earlier publications. Matlab code is available at Bioinformatics online.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
One of the key objectives of computational systems biology is to learn the structure of protein activation pathways and gene regulatory networks
To avoid that the non-homogeneous dynamic Bayesian networks (DBNs) (NH-DBNs) reduce to a DBN when the percentages of coupled edges approach 100%, we assume the changepoints to be known, so that the changepoints do not have to be inferred from the data
We propose to use a principal component analysis (PCA) and a cluster analysis to visualizesimilarities between the NH-DBNs from Section 2.6
Summary
One of the key objectives of computational systems biology is to learn the structure of protein activation pathways and gene regulatory networks. For modelling time-varying regulatory networks many non-homogeneous DBNs (NH-DBNs) have been proposed in the literature Those NH-DBN models can be divided into two conceptual groups: (i) NH-DBNs that only allow the network parameters to vary in time (see references below) and (ii) NH-DBNs that allow even the network structure to be timedependent (see, e.g. Husmeier et al, 2010; Lebre et al, 2010; Robinson and Hartemink, 2010). The latter group (ii) offers great model flexibility, but faces a practical and a conceptual problem. An edge from gene Zi to gene Zj in a gene regulatory network indicates that gene Zi codes for a transcription factor that can bind
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