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.

Meeting Name

IEEE International Joint Conference on Neural Networks, 2005


Electrical and Computer Engineering

Second Department

Computer Science

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

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2005