841133
doi
10.5281/zenodo.841133
oai:zenodo.org:841133
user-precise
user-eu
Koeppl, Heinz
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
Al-Sayed, Sara
Department of Electrical Engineering, Technische Universität Darmstadt, Germany
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
<p>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<br>
non-uniformly–spaced intervals; missing data; and computational complexity of inference, parameter estimation, and sparse structure search.</p>
Zenodo
2017-07-22
info:eu-repo/semantics/conferencePoster
841132
user-precise
user-eu
award_title=PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION; award_number=668858; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/668858; funder_id=00k4n6c32; funder_name=European Commission;
1579533723.595089
759111
md5:d6f85362246ced3d610c19686941f12c
https://zenodo.org/records/841133/files/ISMB-2017-Fast-biological-network.pdf
public
10.5281/zenodo.841132
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doi