A study of particle swarm optimization in gene regulatory networks inference

Authors:
Rui Xu;Ganesh Venayagamoorthy;Donald C. Wunsch
Affiliations:
Applied Computational Intelligence Laboratory, University of Missouri, Rolla, MO;Real-Time Power and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, University of Missouri, Rolla, MO;Applied Computational Intelligence Laboratory, University of Missouri, Rolla, MO
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 4
Cited 1

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Field Guide to Dynamical Recurrent Networks
Reconstructing gene networks from large scale gene expression data

Reconstructing gene networks from large scale gene expression data
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization

Neural Networks

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Abstract

Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.