Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts from artificial intelligence and machine learning. Here, we use a recurrent neural network (RNN), trained with particle swarm optimization (PSO), to investigate the behaviors of regulatory networks. The experimental results, on a synthetic data set and a real data set, show that the proposed model and algorithm can effectively capture the dynamics of the gene expression time series and are capable of revealing regulatory interactions between genes.
R. Xu and D. C. Wunsch, "Gene Regulatory Networks Inference with Recurrent Neural Network Models," Proceedings of the IEEE International Joint Conference on Neural Networks, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2005.1555844
IEEE International Joint Conference on Neural Networks, 2005
Electrical and Computer Engineering
Keywords and Phrases
DNA; DNA Microarray Data; Cellular Processes; Computational Tools; Gene Functions; Gene Regulatory Networks Inference; Genetics; Inference Mechanisms; Large-Scale Time Series Gene Expression Data; Particle Swarm Optimization; Recurrent Neural Nets; Recurrent Neural Network Models; Regulatory Interactions; Time Series
Article - Conference proceedings
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