title: Tumor classification based on non-negative matrix factorization using gene expression data.
abstract: 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.
authors: College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China. zhengch@126.com
authors: Chun-Hou
authors: CH
authors: Zheng
authors: To-Yee
authors: TY
authors: Ng
authors: Lei
authors: L
authors: Zhang
authors: Chi-Keung
authors: CK
authors: Shiu
authors: Hong-Qiang
authors: HQ
authors: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; 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