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Metasample-Based Sparse Representation for Tumor Classification

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5 Author(s)
Chun-Hou Zheng ; Coll. of Inf. & Commun. Technol., Qufu Normal Univ., Rizhao, China ; Zhang, D. ; To-Yee Ng ; Shiu, S.C.K.
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A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l1-regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l1-norm minimization leads to a sparse solution, the proposed method is called metasample-based SR classification (MSRC). Extensive experiments on publicly available gene expression data sets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.

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Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:8 ,  Issue: 5 )