title: Metasample-based sparse representation for tumor classification.
abstract: 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.
authors.affiliation: College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong 276826, China. zhengch99@126.com
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.forename: To-Yee
authors.initials: TY
authors.lastname: Ng
authors.forename: Simon C K
authors.initials: SC
authors.lastname: Shiu
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine