Published July 22, 2017
| Version v1
Poster
Open
Inferring network statistics from high-dimensional undersampled time-course data
Creators
- 1. Department of Electrical Engineering, Technische Universität Darmstadt, Germany
- 2. Department of Electrical Engineering and Department of Biology, Technische Universität Darmstadt, Germany
Description
Reconstructing networks from current high-dimensional datasets is a notoriously ill-posed problem. For this reason we want to focus on more general statistical properties, as finding the degree distribution or sparsity. We consider sub-systems of continuously valued random variables embedded in a larger hidden system. In this scenario, also referred to as undersampling, the dynamics of the sub-systems are influenced by the hidden system. With increasing number of connections between sub-system and hidden system these effects can be treated statistically. Analysing these effects then allows for inference of statistical properties of the hidden system.
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ISMB-2017-Inferring-network-statistics-TUDA-Linzner.pdf
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