Tumor Classification Based on Non-Negative Matrix Factorization Using Gene Expression Data

被引：42
作者：
Zheng, Chun-Hou [1 ,2 ]
Ng, To-Yee
Zhang, Lei [1 ]
Shiu, Chi-Keung
Wang, Hong-Qiang [3 ]
机构：
[1] Hong Kong Polytech Univ, Dept Comp, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
[2] Anhui Univ, Coll Elect Engn & Automat, Hefei 230039, Anhui, Peoples R China
[3] Chinese Acad Sci, Intelligent Comp Lab, Hefei Inst Intelligent Machines, Hefei, Anhui, Peoples R China
来源：
IEEE TRANSACTIONS ON NANOBIOSCIENCE | 2011年 / 10卷 / 02期
基金：
美国国家科学基金会;
关键词：
Gene expression data; gene selection; nonnegative matrix factorization; tumor classification; INDEPENDENT COMPONENT ANALYSIS; MICROARRAY DATA; MOLECULAR CLASSIFICATION; CLASS DISCOVERY; LEAST-SQUARES; CANCER; SELECTION; REGRESSION;
D O I：
10.1109/TNB.2011.2144998
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using nonnegative matrix factorization (NMF) or sparse NMF (SNMF), and then we extract features from the selected genes by virtue of NMF or SNMF. At last, we apply support vector machines (SVM) to classify the tumor samples using the extracted features. In order for a better classification, a modified SNMF algorithm is also proposed. The experimental results on benchmark three microarray data sets validate that the proposed method is efficient. Moreover, the biological meaning of the selected genes are also analyzed.
引用
收藏
页码：86 / 93
页数：8
相关论文
共 41 条
[1]   Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays [J].
Alon, U ;
Barkai, N ;
Notterman, DA ;
Gish, K ;
Ybarra, S ;
Mack, D ;
Levine, AJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1999, 96 (12) :6745-6750
[2]   Singular value decomposition for genome-wide expression data processing and modeling [J].
Alter, O ;
Brown, PO ;
Botstein, D .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2000, 97 (18) :10101-10106
[3]   Face recognition by independent component analysis [J].
Bartlett, MS ;
Movellan, JR ;
Sejnowski, TJ .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (06) :1450-1464
[4]   PCA disjoint models for multiclass cancer analysis using gene expression data [J].
Bicciato, S ;
Luchini, A ;
Di Bello, C .
BIOINFORMATICS, 2003, 19 (05) :571-578
[5]   Molecular classification of cutaneous malignant melanoma by gene expression profiling [J].
Bittner, M ;
Meitzer, P ;
Chen, Y ;
Jiang, Y ;
Seftor, E ;
Hendrix, M ;
Radmacher, M ;
Simon, R ;
Yakhini, Z ;
Ben-Dor, A ;
Sampas, N ;
Dougherty, E ;
Wang, E ;
Marincola, F ;
Gooden, C ;
Lueders, J ;
Glatfelter, A ;
Pollock, P ;
Carpten, J ;
Gillanders, E ;
Leja, D ;
Dietrich, K ;
Beaudry, C ;
Berens, M ;
Alberts, D ;
Sondak, V ;
Hayward, N ;
Trent, J .
NATURE, 2000, 406 (6795) :536-540
[6]   Metagenes and molecular pattern discovery using matrix factorization [J].
Brunet, JP ;
Tamayo, P ;
Golub, TR ;
Mesirov, JP .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (12) :4164-4169
[7]   INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].
COMON, P .
SIGNAL PROCESSING, 1994, 36 (03) :287-314
[8]  
Cordero Francesca, 2007, Briefings in Functional Genomics & Proteomics, V6, P265, DOI 10.1093/bfgp/elm034
[9]  
Daniela G.C., 2006, STUDIES CLASSIFICATI, P21
[10]   Comparison of discrimination methods for the classification of tumors using gene expression data [J].
Dudoit, S ;
Fridlyand, J ;
Speed, TP .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2002, 97 (457) :77-87
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