Molecular biology has revealed that complex life phenomena can be treated as the result of many gene interactions. Investigating these interactions and understanding the intrinsic mechanisms of biological systems using gene expression data have attracted a lot of attention. As a typical gene regulatory network (GRN) inference method, the S-system has been utilized to deal with small-scale network identification. However, it is extremely difficult to optimize it to infer medium-to-large networks. This paper proposes a novel parallel swarm intelligent algorithm, PGRNIG, to optimize the parameters of the S-system. We employed the clone selection strategy to improve the whale optimization algorithm (CWOA). To enhance the time efficiency of CWOA optimization, we utilized a parallel CWOA (PCWOA) based on the compute unified device architecture (CUDA) platform. Decomposition strategy and L1 regularization were utilized to reduce the search space and complexity of GRN inference. We applied the PGRNIG algorithm on three synthetic datasets and two real time-series expression datasets of the species of Escherichia coli and Saccharomyces cerevisiae. Experimental results show that PGRNIG could infer the gene regulatory network more accurately than other state-of-the-art methods with a convincing computational speed-up. Our findings show that CWOA and PCWOA have faster convergence performances than WOA.

• PMID: 36064791
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Journal Article | Research Support, Non-U.S. Gov't

Authors

Yang B, Bao W, Chen B

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