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ARTICLE
A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models
Yan Li1,2, Dayou Liu1,2, Yungang Zhu1,2, Jie Liu1,2,*
1 College of Computer Science and Technology, Jilin University, Changchun, 130012, China
2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
* Corresponding Author: Jie Liu. Email:
(This article belongs to the Special Issue: Data Science and Modeling in Biology, Health, and Medicine)
Computer Modeling in Engineering & Sciences 2020, 124(1), 303-313. https://doi.org/10.32604/cmes.2020.09353
Received 05 December 2019; Accepted 20 March 2020; Issue published 19 June 2020
Abstract
Under different conditions, gene regulatory networks (GRNs) of the
same gene set could be similar but different. The differential analysis of GRNs
under different conditions is important for understanding condition-specific gene
regulatory relationships. In a naive approach, existing GRN inference algorithms
can be used to separately estimate two GRNs under different conditions and identify the differences between them. However, in this way, the similarities between
the pairwise GRNs are not taken into account. Several joint differential analysis
algorithms have been proposed recently, which were proved to outperform the
naive approach apparently. In this paper, we model the GRNs under different conditions with structural equation models (SEMs) to integrate gene expression data
and genetic perturbations, and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure. Then, a Bayesian inference
method is used to make joint differential analysis by solving the integrated model.
We evaluated the performance of the proposed re-parametrization-based Bayesian
differential analysis (ReBDA) algorithm by running simulations on synthetic data
with different settings. The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for
SEMs ReDNet obviously. In the end, the ReBDA algorithm was applied to make
differential analysis on a real human lung gene data set to illustrate its applicability
and practicability.
Keywords
Cite This Article
APA Style
Li, Y., Liu, D., Zhu, Y., Liu, J. (2020). A re-parametrization-based bayesian differential analysis algorithm for gene regulatory networks modeled with structural equation models. Computer Modeling in Engineering & Sciences, 124(1), 303-313. https://doi.org/10.32604/cmes.2020.09353
Vancouver Style
Li Y, Liu D, Zhu Y, Liu J. A re-parametrization-based bayesian differential analysis algorithm for gene regulatory networks modeled with structural equation models. Comput Model Eng Sci. 2020;124(1):303-313 https://doi.org/10.32604/cmes.2020.09353
IEEE Style
Y. Li, D. Liu, Y. Zhu, and J. Liu "A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models," Comput. Model. Eng. Sci., vol. 124, no. 1, pp. 303-313. 2020. https://doi.org/10.32604/cmes.2020.09353