Reverse Engineering of Gene Regulatory Networks Combining Dynamic Bayesian Networks and Prior Biological Knowledge

2018 ◽  
pp. 323-336 ◽  
Author(s):  
Mariana C. de Souza ◽  
Carlos H. A. Higa
Keyword(s):  
Reverse Engineering ◽  
Bayesian Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Dynamic Bayesian Networks ◽  
Biological Knowledge ◽  
Prior Biological Knowledge ◽  
Gene Regulatory

Bayesian Networks for Modeling and Inferring Gene Regulatory Networks

2010 ◽  
pp. 57-78
Author(s):  
Sebastian Bauer ◽  
Peter Robinson
Keyword(s):  
Bayesian Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Search Space ◽  
Dynamic Bayesian Networks ◽  
Underlying Structure ◽  
Mathematical Framework ◽  
Bayesian Techniques ◽  
Inference Algorithms ◽  
Gene Regulatory

Bayesian networks have become a commonly used tool for inferring structure of gene regulatory networks from gene expression data. In this framework, genes are mapped to nodes of a graph, and Bayesian techniques are used to determine a set of edges that best explain the data, that is, to infer the underlying structure of the network. This chapter begins with an explanation of the mathematical framework of Bayesian networks in the context of reverse engineering of genetic networks. The second part of this review discusses a number of variations upon the basic methodology, including analysis of discrete vs. continuous data or static vs. dynamic Bayesian networks, different methods of exploring the potentially huge search space of network structures, and the use of priors to improve the prediction performance. This review concludes with a discussion of methods for evaluating the performance of network structure inference algorithms.

scholarly journals A survey of models for inference of gene regulatory networks

2013 ◽  
Vol 18 (4) ◽  
pp. 444-465 ◽  
Author(s):  
Blagoj Ristevski
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Boolean Networks ◽  
Biological Knowledge ◽  
Advantages And Disadvantages ◽  
Current Trends ◽  
Prior Biological Knowledge ◽  
Validation Criteria ◽  
Gene Regulatory ◽  
Novel Model

In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper.

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