کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1244798 | 969702 | 2009 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. To circumvent the problem, a new gene mining approach is proposed based on the similarity between probability density functions on each gene for the class of interest with respect to the others. This method allows the ascertainment of significant genes that are informative for discriminating each individual class rather than maximizing the separability of all classes. Then one can select genes containing important information about the particular subtypes of diseases. Based on the mined significant genes for individual classes, a support vector machine with local kernel transform is constructed for the classification of different diseases. The combination of the gene mining approach with support vector machine is demonstrated for cancer classification using two public data sets. The results reveal that significant genes are identified for each cancer, and the classification model shows satisfactory performance in training and prediction for both data sets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Talanta - Volume 79, Issue 2, 15 July 2009, Pages 260-267
Journal: Talanta - Volume 79, Issue 2, 15 July 2009, Pages 260-267
نویسندگان
Li-Juan Tang, Jian-Hui Jiang, Hai-Long Wu, Guo-Li Shen, Ru-Qin Yu,