Sparse Representation for Classification of Tumors Using Gene Expression Data
Joint Authors
Source
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-02-23
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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 the l1-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.
American Psychological Association (APA)
Hang, Xiyi& Wu, Fang-Xiang. 2009. Sparse Representation for Classification of Tumors Using Gene Expression Data. BioMed Research International،Vol. 2009, no. 2009, pp.1-6.
https://search.emarefa.net/detail/BIM-988362
Modern Language Association (MLA)
Hang, Xiyi& Wu, Fang-Xiang. Sparse Representation for Classification of Tumors Using Gene Expression Data. BioMed Research International No. 2009 (2009), pp.1-6.
https://search.emarefa.net/detail/BIM-988362
American Medical Association (AMA)
Hang, Xiyi& Wu, Fang-Xiang. Sparse Representation for Classification of Tumors Using Gene Expression Data. BioMed Research International. 2009. Vol. 2009, no. 2009, pp.1-6.
https://search.emarefa.net/detail/BIM-988362
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-988362