scholarly journals Structural network inference from time-series data using a generative model and transfer entropy

2019 ◽  
Vol 125 ◽  
pp. 357-363 ◽  
Author(s):  
Zhihong Zhang ◽  
Genzhou Zhang ◽  
Zhonghao Zhang ◽  
Guo Chen ◽  
Yangbin Zeng ◽  
...  
Keyword(s):  
Time Series ◽  
Network Inference ◽  
Time Series Data ◽  
Generative Model ◽  
Transfer Entropy ◽  
Series Data ◽  
Structural Network

scholarly journals TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy

2011 ◽  
Vol 12 (1) ◽  
pp. 119 ◽  
Author(s):  
Michael Lindner ◽  
Raul Vicente ◽  
Viola Priesemann ◽  
Michael Wibral
Keyword(s):  
Time Series ◽  
Open Source ◽  
Information Flow ◽  
Time Series Data ◽  
Transfer Entropy ◽  
Series Data

scholarly journals Successful network inference from time-series data using mutual information rate

2016 ◽  
Vol 26 (4) ◽  
pp. 043102 ◽  
Author(s):  
E. Bianco-Martinez ◽  
N. Rubido ◽  
Ch. G. Antonopoulos ◽  
M. S. Baptista
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Information Rate

scholarly journals Windowed Granger causal inference strategy improves discovery of gene regulatory networks

2018 ◽  
Vol 115 (9) ◽  
pp. 2252-2257 ◽  
Author(s):  
Justin D. Finkle ◽  
Jia J. Wu ◽  
Neda Bagheri
Keyword(s):  
Time Series ◽  
Network Structure ◽  
Time Delays ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Directed Network ◽  
Rapid Characterization

Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.

scholarly journals Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Senyan Yang ◽  
Lianju Ning ◽  
Xilong Cai ◽  
Mingyu Liu
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Network Traffic ◽  
Road Network ◽  
Time Series Data ◽  
Sliding Window ◽  
Transfer Entropy ◽  
Series Data ◽  
Window Approach ◽  
Road Segments

With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

IMPROVING GENE NETWORK INFERENCE BY COMPARING EXPRESSION TIME-SERIES ACROSS SPECIES, DEVELOPMENTAL STAGES OR TISSUES

2004 ◽  
Vol 02 (04) ◽  
pp. 765-783 ◽  
Author(s):  
GUILLAUME BOURQUE ◽  
DAVID SANKOFF
Keyword(s):  
Time Series ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Interaction Coefficients ◽  
Gene Network Inference ◽  
Linear Differential

We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.

scholarly journals Causal network inference from gene transcriptional time-series response to glucocorticoids

2021 ◽  
Vol 17 (1) ◽  
pp. e1008223
Author(s):  
Jonathan Lu ◽  
Bianca Dumitrascu ◽  
Ian C. McDowell ◽  
Brian Jo ◽  
Alejandro Barrera ◽  
...  
Keyword(s):  
Time Series ◽  
Regulatory Network ◽  
Gene Network ◽  
Network Inference ◽  
Time Series Data ◽  
Elastic Net ◽  
Series Data ◽  
Causal Network ◽  
Causal Relationships ◽  
Gene Regulatory Network Inference

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.

scholarly journals Causal Network Inference from Gene Transcriptional Time Series Response to Glucocorticoids

2019 ◽  
Author(s):  
Jonathan Lu ◽  
Bianca Dumitrascu ◽  
Ian C. McDowell ◽  
Brian Jo ◽  
Alejandro Barrera ◽  
...  
Keyword(s):  
Time Series ◽  
Regulatory Network ◽  
Gene Network ◽  
Network Inference ◽  
Time Series Data ◽  
Elastic Net ◽  
Series Data ◽  
Causal Network ◽  
Causal Relationships ◽  
Gene Regulatory Network Inference

AbstractGene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately paving the way for regulatory network re-engineering. Network inference from transcriptional time series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance but additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time series data of 2, 768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2, 768 genes and 31, 945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is freely available as an open source software package athttps://github.com/lujonathanh/BETS.

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