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<\/p>\nTime-series expression data from genes or proteins always provides us with an indispensable understanding of the temporal dynamics of biological systems. Integrating multi-omics data with biological networks like gene-gene association, protein-protein interaction drug-disease interaction, etc., may be a crucial and effective approach for the interpretation of dynamic expression changes of genes in evolving conditions. The network-based temporal analysis briefs about the temporal changes in molecular pathways, help to characterize molecular activity in circadian rhythm or cell cycle and plays a key role in the cellular stimulus.<\/p>\n
Assimilating temporal expression level information with a biological network is more challenging. In order to gain a broader understanding, interactomes need to be integrated with temporal -omics data. Popular network analysis approaches combine time-varying expression data in network motif finding, active subnetworks identification, community detection, identification of active subnetworks, and identify changes in network features in terms of several centrality measures.<\/p>\n
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Most of the multi-omics data integration tools focused mainly on the static nature of the networks and thus are not applicable in temporal network data. Temporal network analysis tries to determine the regulating factors by exploring the network with varying time duration structure considering the time as a key component of the structure. Recently, the developed tool, TimeNexus, <\/em>is able to estimate time-series data on the sequential layers of any multilayer network. TimeNexus allows users to efficiently create, visualize and manage time-varying multilayer networks by employing different combinations of edge and node tables.<\/p>\n