title: RPCA-Based Tumor Classification Using Gene Expression Data.
abstract: Microarray techniques have been used to delineate cancer groups or to identify candidate genes for cancer prognosis. As such problems can be viewed as classification ones, various classification methods have been applied to analyze or interpret gene expression data. In this paper, we propose a novel method based on robust principal component analysis (RPCA) to classify tumor samples of gene expression data. Firstly, RPCA is utilized to highlight the characteristic genes associated with a special biological process. Then, RPCA and RPCA+LDA (robust principal component analysis and linear discriminant analysis) are used to identify the features. Finally, support vector machine (SVM) is applied to classify the tumor samples of gene expression data based on the identified features. Experiments on seven data sets demonstrate that our methods are effective and feasible for tumor classification.
authors.forename: Jin-Xing
authors.initials: JX
authors.lastname: Liu
authors.forename: Yong
authors.initials: Y
authors.lastname: Xu
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.forename: Heng
authors.initials: H
authors.lastname: Kong
authors.forename: Zhi-Hui
authors.initials: ZH
authors.lastname: Lai
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D025341:Principal Component Analysis