Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information

Wang, Jianxin; Chen, Bin; Wang, Yaqun; Wang, Ningtao; Garbey, Marc; Tran‐Son‐Tay, Roger; Berceli, Scott A.; Wu, Rongling

doi:10.1093/nar/gkt147

Cited by 38 publications

(34 citation statements)

References 30 publications

(46 reference statements)

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“…Bayesian network model [3] and the mutual information association model [4] are commonly used to construct the gene regulation network. The static Bayesian network model is introducing the joint probability distribution to construct the directed acyclic graph.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing Gene Regulation Network based on Conditional Mutual Information

Yang¹,

Xu²

2017

Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)

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Abstract. The purpose of gene regulatory network construction is to deduce the potential relationship between genes in gene expression data. In this paper, the algorithm CMICRAT is used to construct the gene-directed undirected graphs based on the mutual information and Conditional mutual information by the gene expression data, and then use the CRAE to determine the direction of the undirected graph. The experimental results show that the proposed algorithm can improve the accuracy of constructing the gene regulation network by experimentally validating the gene expression data of DREAM4 gene expression data in international biological competition.

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Section: Related Workmentioning

confidence: 99%

Reconstructing Gene Regulation Network based on Conditional Mutual Information

Yang¹,

Xu²

2017

Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)

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“…In general, mutual information (MI) is a natural generalization of the correlation since it can measure the nonlinear dependency and topology sparseness between variables [15]. Here, we use MI to measure the similarity between two species and obtain the significant pairwise relationships with permutation test.…”

Section: Topology Analysis Of Four Seasonal Microbial Correlation Netmentioning

confidence: 99%

Exploring the interaction patterns among taxa and environments from marine metagenomic data

2016

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The sequencing revolution driven by high-throughput technologies has generated a huge amount of marine microbial sequences which hide the interaction patterns among microbial species and environment factors. Exploring these patterns is helpful for exploiting the marine resources. In this paper, we use the complex network approach to mine and analyze the interaction patterns of marine taxa and environments in spring, summer, fall and winter seasons. With the 16S rRNA pyrosequencing data of 76 time point taken monthly over 6 years, we first use our MtHc clustering algorithm to generate the operational taxonomic units (OTUs). Then, employ the k-means method to divide 76 time point samples into four seasonal groups, and utilize mutual information (MI) to construct the four correlation networks among microbial species and environment factors. Finally, we adopt the symmetrical non-negative matrix factorization method to detect the interaction patterns, and analysis the relationship between marine species and environment factors. The results show that the four seasonal microbial interaction networks have the characters of complex networks, and interaction patterns are related with the seasonal variability; the same environmental factor influences different species in the four seasons; the four environmental factors of day length, photosynthetically active radiation, NO 2 + NO 3 and silicate may have stronger influences on microbes than other environment factors.

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“…A new model for the GRNs has been developed in [346], which builds upon existing models by adding an epigenetic control layer. An approach for network inference by integrating expression plasticity into Shannon's mutual information is described in [356] for reconstruction of the GRNs. An integrated method has been developed for reconstructing the GRNs [88], utilizing both temporal information arriving from time-series gene expression profiles and topological properties of protein networks.…”

Section: Inference Of Gene Regulatory Networkmentioning

confidence: 99%