Conference paper Open Access

Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

Al- Sayed, Sara; Koeppl, Heinz

In this work, we appropriate the popular tool of Gaussian processes to solve the problem of reconstructing networks from time-series
perturbation data. To this end, we propose a construction for multivariate Gaussian processes to describe the continuous-time trajectories of the states of the network entities. We then show that this  construction admits a state-space representation for the network dynamics. By exploiting Kalman filtering techniques, we are able to infer the underlying network in a computationally efficient manner.

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