Inner composition alignment for inferring directed networks from short time series
- Identifying causal links (couplings) is a fundamental problem that facilitates the understanding of emerging structures in complex networks. We propose and analyze inner composition alignment-a novel, permutation-based asymmetric association measure to detect regulatory links from very short time series, currently applied to gene expression. The measure can be used to infer the direction of couplings, detect indirect (superfluous) links, and account for autoregulation. Applications to the gene regulatory network of E. coli are presented.
| Author details: | Stefan Hempel, Aneta Koseska, Jürgen KurthsORCiDGND, Zoran NikoloskiORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1103/PhysRevLett.107.054101 |
| ISSN: | 0031-9007 |
| Title of parent work (English): | Physical review letters |
| Publisher: | American Physical Society |
| Place of publishing: | College Park |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2011 |
| Publication year: | 2011 |
| Release date: | 2017/03/26 |
| Volume: | 107 |
| Issue: | 5 |
| Number of pages: | 4 |
| Funding institution: | German BMBF [0313924] |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
| Peer review: | Referiert |
