title: A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.
abstract: In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China; School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221000, Jiangsu, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China; Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China.
authors.forename: Jin-Xing
authors.initials: JX
authors.lastname: Liu
authors.affiliation: Library of Qufu Normal University, Qufu Normal University, Rizhao, 276826, Shandong, China.
authors.forename: Ying-Lian
authors.initials: YL
authors.lastname: Gao
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China.
authors.forename: Xiang-Zhen
authors.initials: XZ
authors.lastname: Kong
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221000, Jiangsu, China; The Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation Chinese Academy of Sciences, Beijing, 100000, China.
authors.forename: Xue-Song
authors.initials: XS
authors.lastname: Wang
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D018506:Gene Expression Regulation, Plant; D017343:Genes, Plant; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010944:Plants / Q000235:genetics*