Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular networks are typically “re-wired” over time in response to cellular development and environmental changes. In our previous work, we formulated the inference of time-varying or dynamic networks as a tracking problem, where the target state is the ensemble of edges in the network. We used the Kalman filter to track the network topology over time. Unfortunately, the output of the Kalman filter does not reflect known properties of molecular networks, such as sparsity.
Ditzler, G., Bouaynaya, N., Shterenberg, R. & Fathaliah-Shaykh, H. (2019). Approximate kernel reconstruction for time-varying networks. BioData Mining 12, 5 doi:10.1186/s13040-019-0192-1
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