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| Title: | Gene regulatory networks inference with recurrent neural network models | |
| Author (s): | Rui Xu Wunsch, Donald C. | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering | |
| Keywords: | DNA DNA microarray data cellular processes computational tools gene functions gene regulatory networks inference genetics inference mechanisms large-scale time series gene expression data particle swarm optimization recurrent neural nets recurrent neural network models regulatory interactions time series | |
| Issue Date: | 2005 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Rui Xu; Wunsch, D.C., II, "Gene regulatory networks inference with recurrent neural network models" IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, pp. 286- 291 vol. 1, 31 July-4 Aug. 2005 | |
| Abstract: | Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts from artificial intelligence and machine learning. Here, we use a recurrent neural network (RNN), trained with particle swarm optimization (PSO), to investigate the behaviors of regulatory networks. The experimental results, on a synthetic data set and a real data set, show that the proposed model and algorithm can effectively capture the dynamics of the gene expression time series and are capable of revealing regulatory interactions between genes. | |
| Type: | Article - Conference proceedings 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 | Gene regulatory networks inference with recurrent neural network models | |
| contributor.author | Rui Xu | |
| contributor.author | Wunsch, Donald C. | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| subject | DNA | |
| subject | DNA microarray data | |
| subject | cellular processes | |
| subject | computational tools | |
| subject | gene functions | |
| subject | gene regulatory networks inference | |
| subject | genetics | |
| subject | inference mechanisms | |
| subject | large-scale time series gene expression data | |
| subject | particle swarm optimization | |
| subject | recurrent neural nets | |
| subject | recurrent neural network models | |
| subject | regulatory interactions | |
| subject | time series | |
| date.issued | 2005 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Rui Xu; Wunsch, D.C., II, "Gene regulatory networks inference with recurrent neural network models" IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, pp. 286- 291 vol. 1, 31 July-4 Aug. 2005 | |
| identifier.pub.URI | ||
| description.abstract | Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts from artificial intelligence and machine learning. Here, we use a recurrent neural network (RNN), trained with particle swarm optimization (PSO), to investigate the behaviors of regulatory networks. The experimental results, on a synthetic data set and a real data set, show that the proposed model and algorithm can effectively capture the dynamics of the gene expression time series and are capable of revealing regulatory interactions between genes. | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| 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 | 2007-04-05T14:25:14Z | |
| date.available | 2007-04-05T14:25:14Z | |
| identifier.persist.URI | ||
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