Title
A Sparse Bayesian Learning Method for Structural Equation Model-Based Gene Regulatory Network Inference.
Abstract
Gene regulatory networks (GRNs) are underlying networks identified by interactive relationships between genes. Reconstructing GRNs from massive genetic data is important for understanding gene functions and biological mechanism, and can provide effective service for medical treatment and genetic research. A series of artificial intelligence based methods have been proposed to infer GRNs from both gene expression data and genetic perturbations. The accuracy of such algorithms can be better than those models that just consider gene expression data. A structural equation model (SEM), which provides a systematic framework integrating both types of gene data conveniently, is a commonly used model for GRN inference. Considering the sparsity of GRNs, in this paper, we develop a novel sparse Bayesian inference algorithm based on Normal-Equation-Gamma (NEG) type hierarchical prior (BaNEG) to infer GRNs modeled with SEMs more accurately. First, we reparameterize an SEM as a linear type model by integrating the endogenous and exogenous variables; Then, a Bayesian adaptive lasso with a three-level NEG prior is applied to deduce the corresponding posterior mode and estimate the parameters. Simulations on synthetic data are run to compare the performance of BaNEG to some state-of-the-art algorithms, the results demonstrate that the proposed algorithm visibly outperforms the others. What & x2019;s more, BaNEG is applied to infer underlying GRNs from a real data set composed of 47 yeast genes from Saccharomyces cerevisiae to discover potential relationships between genes.
Year
DOI
Venue
2020
10.1109/ACCESS.2020.2976743
IEEE ACCESS
Keywords
DocType
Volume
Mathematical model,Inference algorithms,Gene expression,Numerical analysis,Bayes methods,Data models,Sparse Bayesian learning,high-dimensional data,gene regulatory network,gene expression data,structural equation model
Journal
8
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yan Li100.68
Dayou Liu281468.17
jianfeng chu3198.60
Yungang Zhu4314.52
Jie Liu500.68
Xiao-chun Cheng6129.10