scholarly journals An Empirical Comparison Study on Kernel Based Support Vector Machine for Classification of Gene Expression Data Set

2012 ◽  
Vol 38 ◽  
pp. 1340-1345 ◽  
Author(s):  
Smruti Rekha Das ◽  
Kaberi Das ◽  
Debahuti Mishra ◽  
Kailash Shaw ◽  
Sashikala Mishra
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Gene Expression Data ◽  
Support Vector ◽  
Expression Data ◽  
Comparison Study ◽  
Empirical Comparison ◽  
Data Set

scholarly journals Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

PLoS ONE ◽  
2010 ◽  
Vol 5 (6) ◽  
pp. e11267 ◽  
Author(s):  
Ana Lisa V. Gomes ◽  
Lawrence J. K. Wee ◽  
Asif M. Khan ◽  
Laura H. V. G. Gil ◽  
Ernesto T. A. Marques ◽  
...  
Keyword(s):  
Gene Expression ◽  
Support Vector Machines ◽  
Dengue Fever ◽  
Gene Expression Data ◽  
Support Vector ◽  
Expression Data ◽  
Vector Machines

scholarly journals Sparse Representation for Classification of Tumors Using Gene Expression Data

2009 ◽  
Vol 2009 ◽  
pp. 1-6 ◽  
Author(s):  
Xiyi Hang ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Sparse Representation ◽  
Gene Expression Data ◽  
Least Square Method ◽  
Classification Problem ◽  
Least Square ◽  
Support Vector ◽  
Expression Data ◽  
Classification Of Tumors

Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level. However, cancer-alerted gene expression datasets typically have much more number of genes (features) than that of samples (patients), which imposes a challenge for classification of tumors. In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is computed by thel1-regularized least square method. To investigate its performance, the proposed method is applied to six tumor gene expression datasets and compared with various support vector machine (SVM) methods. The experimental results have shown that the performance of the proposed method is comparable with or better than those of SVMs. In addition, the proposed method is more efficient than SVMs as it has no need of model selection.

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