Planned intervention: On Wednesday April 3rd 05:30 UTC Zenodo will be unavailable for up to 2-10 minutes to perform a storage cluster upgrade.
Published July 22, 2017 | Version v1
Poster Open

Inferring network statistics from high-dimensional undersampled time-course data

  • 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.

Files

ISMB-2017-Inferring-network-statistics-TUDA-Linzner.pdf

Files (868.0 kB)

Additional details

Funding

PrECISE – PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION 668858
European Commission