Published September 17, 2018
| Version v1
Conference paper
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Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes
Creators
- 1. Department of Electrical Engineering Technische Universität Darmstadt, Germany
- 2. Department of Electrical Engineering and Department of Biology, Technische Universität Darmstadt, Germany
Description
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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