Sparse representation for classification of tumors using gene expression data.
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 e...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 6p - 7 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2009 Regular Issue
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105438736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105438736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2009 Regular Issue pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105438736 2010398052 10.1155/2009/403689 NLM19300522 105438736 ppf: 6p ppct: 1 formats: fmt: @attributes: type: P tig: atl: Sparse representation for classification of tumors using gene expression data. aug: au: Hang X Wu F affil: Department of Electrical and Computer Engineering, California State University, Northridge, CA 91330, USA. sug: subj: Cancer Patients Gene Expression Neoplasms Classification Kruskal-Wallis Test Neoplasms Diagnosis One-Way Analysis of Variance Human ab: 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 l(1)-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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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