54996
2017
2017
eng
8
95
article
American Physical Society
College Park
1
2017-05-01
2017-05-01
--
Recurrence measure of conditional dependence and applications
Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Herewe propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.
Physical review : E, Statistical, nonlinear and soft matter physics
10.1103/PhysRevE.95.052206
28618513
2470-0045
2470-0053
wos:2017
052206
WOS:000401234600004
Ramos, AMT (reprint author), Natl Inst Space Res INPE, BR-12227010 Sao Jose De Campos, SP, Brazil.; Ramos, AMT (reprint author), Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany., antonio.ramos@inpe.br
Sao Paulo Research Foundation FAPESP [2015/50122-0, 2014/14229-2, 2015/07373-2]; The Deutsche Forschungsgemeinschaft [DFGIRTG 1740/2]; DFG [RTG 2043/1]; Deutscher Akademischer Austauschdienst (DAAD) [57130097]; Humboldt University of Berlin; Universidad Nacional de Colombia at Medellin - European Commission
2022-05-20T10:27:20+00:00
sword
importub
filename=package.tar
da8ad76176ccb9de9ba571308e98a58b
Ramos, Antonio M. T.
false
true
Antonio M. T. Ramos
Alejandro Builes-Jaramillo
German Poveda
Bedartha Goswami
Elbert E. N. Macau
Jürgen Kurths
Norbert Marwan
Physik
Institut für Physik und Astronomie
Referiert
Import
40092
2017
2017
eng
26
postprint
1
--
2017-09-28
--
Unraveling gene regulatory networks from time-resolved gene expression data
Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.
Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.
Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.
a measures comparison study
urn:nbn:de:kobv:517-opus4-400924
online registration
BMC Bioinformatics 12 (2011). - DOI: 10.1186/1471-2105-12-292
292
CC-BY - Namensnennung 4.0 International
Sabrina Hempel
Aneta Koseska
Zoran Nikoloski
Jürgen Kurths
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
371
eng
uncontrolled
unferring cellular networks
eng
uncontrolled
mutual information
eng
uncontrolled
Escherichia-coli
eng
uncontrolled
cluster-analysis
eng
uncontrolled
series
eng
uncontrolled
algorithms
eng
uncontrolled
inference
eng
uncontrolled
models
eng
uncontrolled
recognition
eng
uncontrolled
variables
Datenverarbeitung; Informatik
Biowissenschaften; Biologie
Medizin und Gesundheit
open_access
Institut für Physik und Astronomie
Referiert
Open Access
BioMed Central
Universität Potsdam
https://publishup.uni-potsdam.de/files/40092/pmnr371_online.pdf