A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules

Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly...

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Main Authors: Wenbo Mu, Damian Roqueiro, Yang Dai
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/197406
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author Wenbo Mu
Damian Roqueiro
Yang Dai
author_facet Wenbo Mu
Damian Roqueiro
Yang Dai
author_sort Wenbo Mu
collection DOAJ
description Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression profiles of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical significance and biological relevance. The identified regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs.
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institution Kabale University
issn 1537-744X
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record_format Article
series The Scientific World Journal
spelling doaj-art-786ebe7097654ab499933504cb9494f02025-02-03T01:31:25ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/197406197406A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene ModulesWenbo Mu0Damian Roqueiro1Yang Dai2Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USADepartment of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USADepartment of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USATranscription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression profiles of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical significance and biological relevance. The identified regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs.http://dx.doi.org/10.1155/2013/197406
spellingShingle Wenbo Mu
Damian Roqueiro
Yang Dai
A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules
The Scientific World Journal
title A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules
title_full A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules
title_fullStr A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules
title_full_unstemmed A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules
title_short A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules
title_sort local genetic algorithm for the identification of condition specific microrna gene modules
url http://dx.doi.org/10.1155/2013/197406
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