Bayesian Inference of Gene Regulatory Networks Using Gene Expression Time Series Data

2007 ◽  
pp. 1-15 ◽  
Author(s):  
Nicole Radde ◽  
Lars Kaderali
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Bayesian Inference ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Series Data ◽  
Gene Expression Time Series ◽  
Gene Regulatory ◽  
Expression Time

Inference of gene regulatory networks based on nonlinear ordinary differential equations

2020 ◽  
Vol 36 (19) ◽  
pp. 4885-4893 ◽  
Author(s):  
Baoshan Ma ◽  
Mingkun Fang ◽  
Xiangtian Jiao
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Differential Equations ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Series Data ◽  
State Data ◽  
Gene Regulatory

Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.

Using gene expression programming to infer gene regulatory networks from time-series data

2013 ◽  
Vol 47 ◽  
pp. 198-206 ◽  
Author(s):  
Yongqing Zhang ◽  
Yifei Pu ◽  
Haisen Zhang ◽  
Yabo Su ◽  
Lifang Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Gene Expression Programming ◽  
Series Data ◽  
Gene Regulatory
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