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dc.contributor.authorYang, Haixuan
dc.identifier.citationYang, Haixuan and Bhat, Prajwal and Shanahan, Hugh and Paccanaro, Alberto (2008) A Maximal Eigenvalue Method for Detecting Process Representative Genes by Integrating Data from Multiple Sources Learning from Multiple Sources Workshop, NIPS 2008en_US
dc.descriptionConference paperen_US
dc.description.abstractAn important problem in computational biology is the identification of candidate genes which can be considered as representative of the different cellular processes taking place in the cell as it evolves through time. Multiple and very noisy data sources contain information about such processes and should therefore be inte-grated in order to obtain a reliable identification of such candidate genes. In this paper, we present a novel ranking algorithm which determines process represen-tative genes by integrating a set of noisy binary relations between genes. We present some preliminary results on two artificial toy datasets and one real biolog-ical problem. In the biological problem, we use this method to identify representa-tive genes of some of the fundamental biological mechanisms taking place during cellular growth in A. thaliana by integrating gene expression data and information from the gene GO annotation.en_US
dc.relation.ispartofLearning from Multiple Sources Workshop, NIPS 2008en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectComputational biologyen_US
dc.subjectA. thalianaen_US
dc.titleA Maximal Eigenvalue Method for Detecting Process Representative Genes by Integrating Data from Multiple Sourcesen_US
dc.typeConference Paperen_US
dc.local.contactHaixuan Yang, School Of Mathematics,Statistics, & Applied Mathematics, Adb-G013, Nui Galway. 2320 Email:

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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland