article

Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization

Authors:

Donald Wunsch II,

Ronald FrankAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 4, Issue 4

Pages 681 - 692

https://doi.org/10.1109/TCBB.2007.1057

Published: 01 October 2007 Publication History

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.

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Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 4, Issue 4

October 2007

192 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 October 2007

Published in TCBB Volume 4, Issue 4

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https://dl.acm.org/doi/10.1007/s11554-020-01061-z
Hurtado SGarcía-Nieto JNavas-Delgado INebro AAldana-Montes J(2021)Reconstruction of gene regulatory networks with multi-objective particle swarm optimisersApplied Intelligence10.1007/s10489-020-01891-151:4(1972-1991)Online publication date: 1-Apr-2021
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Yin FZhou JZhu ZMa XXie W(2021)Inferring Small-Scale Maximum-Entropy Genetic Regulatory Networks by Using DE AlgorithmAdvances in Swarm Intelligence10.1007/978-3-030-78743-1_31(347-357)Online publication date: 17-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-78743-1_31
Khan ADutta ASaha GPal R(2020)A Hybrid Methodology for the Reverse Engineering of Gene Regulatory Networks2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185866(1-8)Online publication date: 19-Jul-2020
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