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Inference of Gene Regulatory Networks from Time Course Gene Expression Data Using Neural Networks and Swarm Intelligence

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5 Author(s)
Ressom, H.W. ; Lombardi Comprehensive Cancer Center, Georgetown Univ., Washington, DC ; Zhang, Y. ; Xuan, J. ; Wang, Y.
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We present a novel algorithm that combines a recurrent neural network (RNN) and two swarm intelligence (SI) methods to infer a gene regulatory network (GRN) from time course gene expression data. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of an RNN, while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN whose response mimics gene expression data generated by time course DNA microarray experiments. We observed promising results in applying the proposed hybrid SI-RNN algorithm to infer networks of interaction from simulated and real-world gene expression data

Published in:

Computational Intelligence and Bioinformatics and Computational Biology, 2006. CIBCB '06. 2006 IEEE Symposium on

Date of Conference:

28-29 Sept. 2006