The gene regulatory network (GRN) is a model for studying the function and behavior of genes by treating the genome as a whole, which can reveal the gene expression mechanism. However, due to the dynamics, nonlinearity, and complexity of gene expression data, it is a challenging task to construct a GRN precisely. In this paper, a combination method of long short-term memory network (LSTM) and mean impact value (MIV) was applied for GRN reconstruction. Firstly, LSTM was employed to establish a gene expression prediction model. To improve the performance of LSTM, a particle swarm optimization (PSO) was introduced to optimize the weight and learning rate. Then, the MIV was used to infer the regulation among genes. In order to test the proposed approach, it was applied to three datasets: a simulated dataset and two real biology datasets. By comparing with other methods, the experimental results indicate that the proposed method has higher modeling accuracy and it can be used to effectively construct a GRN. At last, in view of the fouling-forming problem of slime-forming bacteria (SFB), we have designed electromagnetic field experiments and transcriptome sequencing experiments to locate the fouling-forming genes and obtain gene expression data. And a GRN of fouling-forming genes of SFB was constructed using the proposed approach.
Keywords: Gene regulatory network, Long short-term memory, Particle swarm optimization algorithm, Mean impact value, Slime-forming bacteria, Yeast gene data, Machine learning
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