Inferring Network Interactions Using Recurrent Neural Networks and Swarm Intelligence

We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized u...

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Bibliographic Details
Published in:2006 International Conference of the IEEE Engineering in Medicine and Biology Society 2006, p.4241-4244
Main Authors: Ressom, H.W., Zhang, Y., Xuan, J., Wang, Y., Clarke, R.
Format: Article
Language:English
Subjects:
Ant colony optimization
Artificial neural networks
Biological systems
Computer architecture
Gene expression
Modeling
Neurons
Nonlinear dynamical systems
Particle swarm optimization
Recurrent neural networks
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Summary:We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN that mimics the true structure of an unknown network and the time-series data that the network generated. We applied the proposed hybrid SI-RNN algorithm to infer a simulated genetic network. The results indicate that the algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data
ISSN:1557-170X
DOI:10.1109/IEMBS.2006.259812