Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5903
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dc.contributor.authorPavlidis, SP-
dc.contributor.authorPayne, AM-
dc.contributor.authorSwift, SM-
dc.date.accessioned2011-10-11T13:29:30Z-
dc.date.available2011-10-11T13:29:30Z-
dc.date.issued2011-
dc.identifier.citationAlgorithms for Molecular Biology, 6: 22, 2011en_US
dc.identifier.issn1748-7188-
dc.identifier.urihttp://www.almob.org/content/6/1/22/abstracten
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5903-
dc.descriptionThis article is available through the Brunel Open Access Publishing Fund. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.abstractBackground: Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results: We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions: We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.en_US
dc.description.sponsorshipThe work was sponsored by the studentship scheme of the School of Information Systems, Computing and Mathematics, Brunel Universityen_US
dc.languageEn-
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.subjectGene expression analysisen_US
dc.subjectMicroarray data analysisen_US
dc.subjectGenesen_US
dc.titleMulti-membership gene regulation in pathway based microarray analysisen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1186/1748-7188-6-22-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
Appears in Collections:Publications
Computer Science
Brunel OA Publishing Fund
Dept of Computer Science Research Papers

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