A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks

Xiao Hu; Ann Maglia; Donald C. Wunsch

doi:10.1109/IEMBS.2005.1615529

A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks

Xiao Hu
, Ann Maglia
, Donald C. Wunsch

Missouri University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined by a weight matrix, and different genes are allowed to have different decaying time constants. We demonstrate the effectiveness of the method by recreating the SOS DNA Repair network of Escherichia coli bacterium, previously discovered through experimental data.

Original language	American English
Title of host publication	Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages	4735-4738
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2005.1615529
State	Published - Jan 1 2005
Externally published	Yes
Event	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China Duration: Sep 1 2005 → Sep 4 2005

Publication series

Name	Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China)

Conference

Conference	2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/Territory	China
City	Shanghai
Period	9/1/05 → 9/4/05

ASJC Scopus Subject Areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Disciplines

Biology
Electrical and Computer Engineering

Access to Document

10.1109/IEMBS.2005.1615529License: Unspecified

A General Recurrent Neural Network Approach to Model Genetic ReguFinal published version, 357 KBLicense: Unspecified

Cite this

Hu, X., Maglia, A., & Wunsch, D. C. (2005). A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 (pp. 4735-4738). Article 1615529 (Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China)). https://doi.org/10.1109/IEMBS.2005.1615529

A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks. / Hu, Xiao; Maglia, Ann; Wunsch, Donald C.
Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. p. 4735-4738 1615529 (Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hu, X, Maglia, A & Wunsch, DC 2005, A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks. in Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005., 1615529, Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China), pp. 4735-4738, 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, Shanghai, China, 9/1/05. https://doi.org/10.1109/IEMBS.2005.1615529

Hu X, Maglia A, Wunsch DC. A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks. In Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. p. 4735-4738. 1615529. (Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China)). doi: 10.1109/IEMBS.2005.1615529

Hu, Xiao ; Maglia, Ann ; Wunsch, Donald C. / A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks. Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 2005. pp. 4735-4738 (Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS (2005, Shanghai, China)).

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abstract = " There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined by a weight matrix, and different genes are allowed to have different decaying time constants. We demonstrate the effectiveness of the method by recreating the SOS DNA Repair network of Escherichia coli bacterium, previously discovered through experimental data.",

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AB - There is an urgent need for tools to unravel the complex interactions and functionalities of genes. As such, there has been much interest in reverse-engineering genetic regulatory networks from time series gene expression data. We use an artificial neural network to model the dynamics of complicated gene networks and to learn their parameters. The positive and negative regulations of genes are defined by a weight matrix, and different genes are allowed to have different decaying time constants. We demonstrate the effectiveness of the method by recreating the SOS DNA Repair network of Escherichia coli bacterium, previously discovered through experimental data.

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A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks

Abstract

Publication series

Conference

ASJC Scopus Subject Areas

Disciplines

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