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| Title: | Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization | |
| Author (s): | Xu, Rui Wunsch, Donald C. Frank, Ronald L. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Biological Sciences Electrical and Computer Engineering Intelligent Systems Center | |
| Keywords: | genetic regulatory networks particle swarm optimization recurrent neural networks | |
| Subject Terms: | Gene expression. Neural networks (Computer science) Swarm intelligence. | |
| Issue Date: | 2007 | |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | |
| Citation: | Xu Rui, Donald C. Wunsch, and Ronald L. Frank. "Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization", IEEE IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007. | |
| Abstract: | Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes. | |
| Type: | Article - Journal text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder FULL COPYRIGHT INFORMATION: | |
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| title | Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization | |
| contributor.author | Xu, Rui | |
| contributor.author | Wunsch, Donald C. | |
| contributor.author | Frank, Ronald L. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Biological Sciences | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Intelligent Systems Center | |
| contributor.sponsor | M.K. Finley Missouri Endowment | |
| contributor.sponsor | National Science Foundation | |
| subject | genetic regulatory networks | |
| subject | particle swarm optimization | |
| subject | recurrent neural networks | |
| subject.LCSH | Gene expression. | |
| subject.LCSH | Neural networks (Computer science) | |
| subject.LCSH | Swarm intelligence. | |
| date.issued | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
| identifier.citation | Xu Rui, Donald C. Wunsch, and Ronald L. Frank. "Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization", IEEE IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007. | |
| identifier.pub.URI | ||
| description.abstract | Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes. | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Postprint | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder | |
| rights.URI | ||
| date.accessioned | 2008-05-20T14:24:24Z | |
| date.available | 2008-05-20T14:24:27Z | |
| identifier.persist.URI | ||
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