Inner composition alignment for inferring directed networks from short time series

S. Hempel, A. Koseska, J. Kurths, Z. Nikoloski

Research output: Contribution to journalArticle

93 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number054101
Number of pages4
JournalPhysical Review Letters
Volume107
Issue number5
DOIs
Publication statusPublished - 26 Jul 2011

Fingerprint Dive into the research topics of 'Inner composition alignment for inferring directed networks from short time series'. Together they form a unique fingerprint.

  • Cite this