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dc.contributor.authorAbusamra, Heba
dc.date.accessioned2015-05-07T14:17:37Z
dc.date.available2015-05-07T14:17:37Z
dc.date.issued2013-11-01
dc.identifier.citationA Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma 2013, 23:5 Procedia Computer Science
dc.identifier.issn18770509
dc.identifier.doi10.1016/j.procs.2013.10.003
dc.identifier.urihttp://hdl.handle.net/10754/552481
dc.description.abstractMicroarray gene expression data gained great importance in recent years due to its role in disease diagnoses and prognoses which help to choose the appropriate treatment plan for patients. This technology has shifted a new era in molecular classification. Interpreting gene expression data remains a difficult problem and an active research area due to their native nature of “high dimensional low sample size”. Such problems pose great challenges to existing classification methods. Thus, effective feature selection techniques are often needed in this case to aid to correctly classify different tumor types and consequently lead to a better understanding of genetic signatures as well as improve treatment strategies. This paper aims on a comparative study of state-of-the- art feature selection methods, classification methods, and the combination of them, based on gene expression data. We compared the efficiency of three different classification methods including: support vector machines, k-nearest neighbor and random forest, and eight different feature selection methods, including: information gain, twoing rule, sum minority, max minority, gini index, sum of variances, t-statistics, and one-dimension support vector machine. Five-fold cross validation was used to evaluate the classification performance. Two publicly available gene expression data sets of glioma were used in the experiments. Results revealed the important role of feature selection in classifying gene expression data. By performing feature selection, the classification accuracy can be significantly boosted by using a small number of genes. The relationship of features selected in different feature selection methods is investigated and the most frequent features selected in each fold among all methods for both datasets are evaluated.
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1877050913011381
dc.rightsArchived with thanks to Procedia Computer Science. http://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectgene expression
dc.subjectmicroarray data
dc.subjectfeature selection
dc.subjectclassification
dc.subjectglioma
dc.titleA Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalProcedia Computer Science
dc.conference.date2013-11-07 to 2013-11-09
dc.conference.name4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013
dc.conference.locationSeoul, KOR
dc.eprint.versionPublisher's Version/PDF
kaust.personAbusamra, Heba
refterms.dateFOA2018-06-13T13:20:25Z
dc.date.published-online2013-11-01
dc.date.published-print2013


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