Inference of gene regulatory networks from time course gene expression data using neural networks and swarm intelligence

H. W. Ressom, Y. Zhang, J. Xuan, Y. Wang, R. Clarke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06
Pages435-442
Number of pages8
DOIs
StatePublished - 2006
Externally publishedYes
Event3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB - Toronto, ON, Canada
Duration: Sep 28 2006Sep 29 2006

Publication series

NameProceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB'06

Conference

Conference3rd Computational Intelligence in Bioinformatics and Computational Biology Symposium, CIBCB
Country/TerritoryCanada
CityToronto, ON
Period9/28/069/29/06

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