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 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 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.

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

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.

scholarly journals A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253307
Author(s):  
Charu Sharma ◽  
Niteesh Sahni
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Stock Returns ◽  
High Frequency ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Time Series Data ◽  
Series Data ◽  
Joint Probability Distribution ◽  
Vine Copula

In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall’s Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns.

scholarly journals CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

2015 ◽  
Vol 14 (3) ◽  
Author(s):  
Christopher A. Penfold ◽  
Ahmed Shifaz ◽  
Paul E. Brown ◽  
Ann Nicholson ◽  
David L. Wild
Keyword(s):  
Time Series ◽  
High Performance ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Causal Structure ◽  
Series Data ◽  
Structure Identification ◽  
Multiple Time ◽  
Multiple Time Series

AbstractHere we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.

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