Christoph Echtermeyer

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Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M.(More)
Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber(More)
Two-dimensional difference gel electrophoresis (2-D DIGE) allows for reliable quantification of global protein abundance changes. The threshold of significance for protein abundance changes depends on the experimental variation (biological and technical). This study estimates biological, technical and total variation inherent to 2-D DIGE analysis of(More)
We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning(More)
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