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dc.contributor.authorAbusamra, Heba
dc.contributor.authorBajic, Vladimir B.
dc.date.accessioned2016-07-26T09:28:24Z
dc.date.available2016-07-26T09:28:24Z
dc.date.issued2016-07-20
dc.identifier.citationShay, J. W., Homma, N., Zhou, R., Naseer, M. I., Chaudhary, A. G., Al-Qahtani, M., … Baeesa, S. (2016). Abstracts from the 3rd International Genomic Medicine Conference (3rd IGMC 2015). BMC Genomics, 17(S6). doi:10.1186/s12864-016-2858-0
dc.identifier.doi10.1186/s12864-016-2858-0
dc.identifier.urihttp://hdl.handle.net/10754/617533
dc.description.abstractThe native nature of high dimension low sample size of gene expression data make the classification task more challenging. Therefore, feature (gene) selection become an apparent need. Selecting a meaningful and relevant genes for classifier not only decrease the computational time and cost, but also improve the classification performance. Among different approaches of feature selection methods, however most of them suffer from several problems such as lack of robustness, validation issues etc. Here, we present a new feature selection technique that takes advantage of clustering both samples and genes. Materials and methods We used leukemia gene expression dataset [1]. The effectiveness of the selected features were evaluated by four different classification methods; support vector machines, k-nearest neighbor, random forest, and linear discriminate analysis. The method evaluate the importance and relevance of each gene cluster by summing the expression level for each gene belongs to this cluster. The gene cluster consider important, if it satisfies conditions depend on thresholds and percentage otherwise eliminated. Results Initial analysis identified 7120 differentially expressed genes of leukemia (Fig. 15a), after applying our feature selection methodology we end up with specific 1117 genes discriminating two classes of leukemia (Fig. 15b). Further applying the same method with more stringent higher positive and lower negative threshold condition, number reduced to 58 genes have be tested to evaluate the effectiveness of the method (Fig. 15c). The results of the four classification methods are summarized in Table 11. Conclusions The feature selection method gave good results with minimum classification error. Our heat-map result shows distinct pattern of refines genes discriminating between two classes of leukemia.
dc.publisherSpringer Nature
dc.relation.urlhttp://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2858-0
dc.relation.urlhttps://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-016-2858-0
dc.rightsThis article is an open access distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s).
dc.rightsThis file is an open access version redistributed from: https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-016-2858-0
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleClustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes
dc.typePresentation
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalBMC Genomics
dc.conference.date30 November - 3 December 2015
dc.conference.nameThe 3rd International Genomic Medicine Conference (3rd IGMC 2015)
dc.conference.locationJeddah, Kingdom of Saudi Arabia
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCenter of Excellence in Genomic Medicine Research, King Abdulaziz University, PO Box 80216, Jeddah 21589, Saudi Arabia
kaust.personAbusamra, Heba
kaust.personBajic, Vladimir B.
refterms.dateFOA2020-07-02T12:52:52Z
dc.date.published-online2016-07-20
dc.date.published-print2016-07


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This article is an open access distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s).
Except where otherwise noted, this item's license is described as This article is an open access distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s).