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.

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Metadaten
Author:Stefan Hempel, Aneta Koseska, Jürgen KurthsORCiDGND, Zoran NikoloskiORCiDGND
DOI:https://doi.org/10.1103/PhysRevLett.107.054101
ISSN:0031-9007 (print)
Parent Title (English):Physical review letters
Publisher:American Physical Society
Place of publication:College Park
Document Type:Article
Language:English
Year of first Publication:2011
Year of Completion:2011
Release Date:2017/03/26
Volume:107
Issue:5
Pagenumber:4
Funder:German BMBF [0313924]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
Peer Review:Referiert