10.5281/zenodo.1488636
https://zenodo.org/records/1488636
oai:zenodo.org:1488636
Al- Sayed, Sara
Sara
Al- Sayed
Department of Electrical Engineering Technische Universität Darmstadt, Germany
Koeppl, Heinz
Heinz
Koeppl
Department of Electrical Engineering and Department of Biology, Technische Universität Darmstadt, Germany
Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes
Zenodo
2018
Network reconstruction
multivariate Gaussian processes
state-space representation
time- course data
2018-09-17
10.5281/zenodo.1488635
https://zenodo.org/communities/precise
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
668858
PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION