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Identifying genes relevant to specific biological conditions in time course microarray experiments
Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
Institute for Genetics and Biometry, Leibniz Institute for Farm Animal Biology, Dummerstorf, Germany.ORCID iD: 0000-0002-7173-5579
Institute for Mathematics and Informatics, Ernst Moritz Arndt University of Greifswald, Greifswald, Germany.
Institute for Biostatistics and Informatics in Medicine and Ageing Research, Department of Medicine, University of Rostock, Rostock, Germany.
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2013 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 10, article id e76561Article in journal (Refereed) Published
Abstract [en]

Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call "relative Signal-to-Noise ratio" (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.

Place, publisher, year, edition, pages
San Fransisco, USA: Public Library Science , 2013. Vol. 8, no 10, article id e76561
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
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URN: urn:nbn:se:oru:diva-40244DOI: 10.1371/journal.pone.0076561ISI: 000325819400064PubMedID: 24146889Scopus ID: 2-s2.0-84885404996OAI: oai:DiVA.org:oru-40244DiVA, id: diva2:777906
Available from: 2015-01-09 Created: 2015-01-07 Last updated: 2018-01-11Bibliographically approved

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