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Springerplus
2016 Nov 03;51:1911. doi: 10.1186/s40064-016-3526-1.
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A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin.
Liu L
,
Zhao T
,
Ma M
,
Wang Y
.
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BACKGROUND: Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology.
RESULTS: Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map''s density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks.
CONCLUSION: The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.
Fig. 1. Structure chart of the feed forward neural network
Fig. 2. The flowchart of model architecture and the structure of the paper. The model takes microarray data as input, and will be trained as described in flowchart: finding out the relationship between any one gene and other n − 1 genes, making adjacency matrix, building gene regulatory network and getting the final gene network according to the weight ratio λ. The training is carried on in each group respectively. The network is compared with the common relevant network by the value of parameters and the differential genes determined by the network are compared with that determined by fold_change
Fig. 3. Structure chart of a linear neural network, b initial gene regulatory network and c final gene regulatory network
Fig. 4. Structure chart of the networks with the weight ratio of 0.85 based on a T12 group, b T15 group and c T18 group
Fig. 5. Difference of the different parameters and comparison of differential genes. Parameters of a average degree, b average Path, c modularity, d average clustering coefficient and e map’s density; f Venn diagram between differential genes determined by network and fold_change
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