Inference of Genetic Regulatory Networks with Recurrent Neural Network Models

Rui Xu; Xiao Hu; Donald C. Wunsch

doi:10.1109/IEMBS.2004.1403826

Inference of Genetic Regulatory Networks with Recurrent Neural Network Models

Civil, Architectural and Environmental Engineering

Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

Abstract

Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.

Original language	American English
Pages (from-to)	2905-2908
Number of pages	4
Journal	Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004
Volume	26 IV
DOIs	https://doi.org/10.1109/IEMBS.2004.1403826
State	Published - Jan 1 2004
Event	Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States Duration: Sep 1 2004 → Sep 5 2004

ASJC Scopus Subject Areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Keywords

Backpropagation
Biochemistry
Biology Computing
Cellular Biophysics
Fundamental Cellular Process
Gene Function
Gene Interactions
Gene Relations
Genetic Regulatory Networks
Genetics
Molecular Biophysics
Recurrent Neural Nets
Recurrent Neural Network Models
Time Series
Time Series Gene Expression

Disciplines

Electrical and Computer Engineering

Access to Document

10.1109/IEMBS.2004.1403826License: Unspecified

Inference of Genetic Regulatory Networks with Recurrent Neural NeFinal published version, 420 KBLicense: Unspecified

Cite this

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title = "Inference of Genetic Regulatory Networks with Recurrent Neural Network Models",

abstract = " Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.",

keywords = "Backpropagation, Biochemistry, Biology Computing, Cellular Biophysics, Fundamental Cellular Process, Gene Function, Gene Interactions, Gene Relations, Genetic Regulatory Networks, Genetics, Molecular Biophysics, Recurrent Neural Nets, Recurrent Neural Network Models, Time Series, Time Series Gene Expression",

author = "Rui Xu and Xiao Hu and Wunsch, \{Donald C.\}",

year = "2004",

month = jan,

day = "1",

doi = "10.1109/IEMBS.2004.1403826",

language = "American English",

volume = "26 IV",

pages = "2905--2908",

journal = "Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004",

note = "Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 ; Conference date: 01-09-2004 Through 05-09-2004",

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TY - JOUR

T1 - Inference of Genetic Regulatory Networks with Recurrent Neural Network Models

AU - Xu, Rui

AU - Hu, Xiao

AU - Wunsch, Donald C.

PY - 2004/1/1

Y1 - 2004/1/1

N2 - Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.

AB - Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several mathematical models, including Boolean networks, Bayesian networks, dynamic Bayesian networks, and linear additive regulation models, have been used to explore the behaviors of regulatory networks. In this paper, we investigate the inference of genetic regulatory networks from time series gene expression in the framework of recurrent neural network model.

KW - Backpropagation

KW - Biochemistry

KW - Biology Computing

KW - Cellular Biophysics

KW - Fundamental Cellular Process

KW - Gene Function

KW - Gene Interactions

KW - Gene Relations

KW - Genetic Regulatory Networks

KW - Genetics

KW - Molecular Biophysics

KW - Recurrent Neural Nets

KW - Recurrent Neural Network Models

KW - Time Series

KW - Time Series Gene Expression

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/1089

U2 - 10.1109/IEMBS.2004.1403826

DO - 10.1109/IEMBS.2004.1403826

M3 - Article

VL - 26 IV

SP - 2905

EP - 2908

JO - Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004

JF - Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004

T2 - Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004

Y2 - 1 September 2004 through 5 September 2004

ER -

Inference of Genetic Regulatory Networks with Recurrent Neural Network Models

Abstract

ASJC Scopus Subject Areas

Keywords

Disciplines

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