Comparison of evolutionary algorithms in gene regulatory network model inference.

Hdl Handle:
http://hdl.handle.net/10147/93864
Title:
Comparison of evolutionary algorithms in gene regulatory network model inference.
Authors:
Sîrbu, Alina; Ruskin, Heather J; Crane, Martin
Affiliation:
Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland. asirbu@computing.dcu.ie.
Citation:
Comparison of evolutionary algorithms in gene regulatory network model inference. 2010, 11:59 BMC Bioinformatics
Journal:
BMC bioinformatics
Issue Date:
2010
URI:
http://hdl.handle.net/10147/93864
DOI:
10.1186/1471-2105-11-59
PubMed ID:
20105328
Abstract:
ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.
Language:
en
ISSN:
1471-2105

Full metadata record

DC FieldValue Language
dc.contributor.authorSîrbu, Alinaen
dc.contributor.authorRuskin, Heather Jen
dc.contributor.authorCrane, Martinen
dc.date.accessioned2010-03-08T14:43:46Z-
dc.date.available2010-03-08T14:43:46Z-
dc.date.issued2010-
dc.identifier.citationComparison of evolutionary algorithms in gene regulatory network model inference. 2010, 11:59 BMC Bioinformaticsen
dc.identifier.issn1471-2105-
dc.identifier.pmid20105328-
dc.identifier.doi10.1186/1471-2105-11-59-
dc.identifier.urihttp://hdl.handle.net/10147/93864-
dc.description.abstractABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.-
dc.language.isoenen
dc.titleComparison of evolutionary algorithms in gene regulatory network model inference.en
dc.contributor.departmentCentre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland. asirbu@computing.dcu.ie.en
dc.identifier.journalBMC bioinformaticsen

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