A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference

Rui Xu; Ganesh Venayagamoorthy; Donald C. Wunsch

doi:10.1007/11760191_95

A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference

Rui Xu
, Ganesh Venayagamoorthy
, Donald C. Wunsch

Missouri University of Science and Technology

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

Abstract

Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.

Original language	American English
Pages (from-to)	648-653
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOIs	https://doi.org/10.1007/11760191_95
State	Published - May 1 2006
Externally published	Yes
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: May 28 2006 → Jun 1 2006

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Gene Regulatory Inference
Genetic Engineering
Optimization
Particle Swarm Optimization (PSO)

Disciplines

Electrical and Computer Engineering

Access to Document

10.1007/11760191_95

Cite this

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title = "A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference",

abstract = " Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.",

keywords = "Gene Regulatory Inference, Genetic Engineering, Optimization, Particle Swarm Optimization (PSO)",

author = "Rui Xu and Ganesh Venayagamoorthy and Wunsch, \{Donald C.\}",

year = "2006",

month = may,

day = "1",

doi = "10.1007/11760191\_95",

language = "American English",

pages = "648--653",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

note = "3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

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T1 - A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference

AU - Xu, Rui

AU - Venayagamoorthy, Ganesh

AU - Wunsch, Donald C.

PY - 2006/5/1

Y1 - 2006/5/1

N2 - Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.

AB - Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the performance of particle swarm optimization (PSO) on the reconstruction of gene networks, which is modeled with recurrent neural networks (RNN). The experimental results on a synthetic data set are presented to show the parameter effects of PSO on RNN training and the effectiveness of the proposed method in revealing the gene relations.

KW - Gene Regulatory Inference

KW - Genetic Engineering

KW - Optimization

KW - Particle Swarm Optimization (PSO)

UR - https://www.scopus.com/pages/publications/33745896799

UR - https://www.scopus.com/pages/publications/33745896799#tab=citedBy

U2 - 10.1007/11760191_95

DO - 10.1007/11760191_95

M3 - Article

SN - 0302-9743

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EP - 653

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

Y2 - 28 May 2006 through 1 June 2006

ER -

A Study of Particle Swarm Optimization in Gene Regulatory Networks Inference

Abstract

ASJC Scopus Subject Areas

Keywords

Disciplines

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