TY  - JOUR
A1  - Hempel, Sabrina
A1  - Koseska, Aneta
A1  - Nikoloski, Zoran
T1  - Data-driven reconstruction of directed networks
T2  - The European physical journal : B, Condensed matter and complex systems
N2  - 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.
Y1  - 2013
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/34962
SN  - 1434-6028
VL  - 86
IS  - 6
PB  - Springer
CY  - New York
ER  -