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Data-driven reconstruction of directed networks

  • We investigate the properties of a recently introduced asymmetric association measure, called inner composition alignment (IOTA), aimed at inferring regulatory links (couplings). We show that the measure can be used to determine the direction of coupling, detect superfluous links, and to account for autoregulation. In addition, the measure can be extended to infer the type of regulation (positive or negative). The capabilities of IOTA to correctly infer couplings together with their directionality are compared against Kendall's rank correlation for time series of different lengths, particularly focussing on biological examples. We demonstrate that an extended version of the measure, bidirectional inner composition alignment (biIOTA), increases the accuracy of the network reconstruction for short time series. Finally, we discuss the applicability of the measure to infer couplings in chaotic systems.

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Metadaten
Author:Sabrina Hempel, Aneta Koseska, Zoran NikoloskiORCiDGND
DOI:https://doi.org/10.1140/epjb/e2013-31111-8
ISSN:1434-6028 (print)
Parent Title (English):The European physical journal : B, Condensed matter and complex systems
Publisher:Springer
Place of publication:New York
Document Type:Article
Language:English
Year of first Publication:2013
Year of Completion:2013
Release Date:2017/03/26
Volume:86
Issue:6
Pagenumber:17
Funder:GoFORSYS project [0313924]; Federal Ministry of Education and Research
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Peer Review:Referiert