Institutions | About Us | Help | Gaeilge
rian logo

Go Back
Comparison of evolutionary algorithms in gene regulatory network model inference
Sîrbu, Alina; Ruskin, Heather J.; Crane, Martin
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 di±cult. 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 insu±cient. 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 o®er 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 identi¯ed and a platform for development of appropriate model formalisms is established.
Keyword(s): Bioinformatics; Mathematical models; Artificial intelligence; Statistical physics; Computer simulation; microarray data analysis; time course data; genetic regulatory networks
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Sîrbu, Alina, Ruskin, Heather J. ORCID: 0000-0001-7101-2242 <> and Crane, Martin ORCID: 0000-0001-7598-3126 <> (2010) Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics, 11 (59). pp. 1471-2205. ISSN 1471-2105
Publisher(s): BioMed Central
File Format(s): application/pdf
Related Link(s):,
First Indexed: 2010-03-06 05:07:57 Last Updated: 2019-02-09 06:55:42