A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma
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Conference PaperAuthors
Abusamra, HebaKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2013-11-01Online Publication Date
2013-11-01Print Publication Date
2013Permanent link to this record
http://hdl.handle.net/10754/552481
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Microarray 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.Citation
A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data of Glioma 2013, 23:5 Procedia Computer SciencePublisher
Elsevier BVJournal
Procedia Computer ScienceConference/Event name
4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S1877050913011381ae974a485f413a2113503eed53cd6c53
10.1016/j.procs.2013.10.003