10.5281/zenodo.841133
https://zenodo.org/records/841133
oai:zenodo.org:841133
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
Fast biological network reconstruction from high-dimensional time-course perturbation data using sparse multivariate Gaussian processes
Zenodo
2017
2017-07-22
Poster
10.5281/zenodo.841132
https://zenodo.org/communities/precise
https://zenodo.org/communities/eu
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
Time-course data observed under the perturbation of biological systems contain rich information about the salient structure of interconnectivity among the entities of the network underlying the system. Challenges: few noisy high-dimensional measurements at
non-uniformly–spaced intervals; missing data; and computational complexity of inference, parameter estimation, and sparse structure search.
European Commission
10.13039/501100000780
668858
PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION