Tumor classification based on non-negative matrix factorization using gene expression data

Chun Hou Zheng, Vincent To Yee Ng, Lei Zhang, Chi Keung Simon Shiu, Hong Qiang Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

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.
Original languageEnglish
Article number5942177
Pages (from-to)86-93
Number of pages8
JournalIEEE Transactions on Nanobioscience
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Jun 2011

Keywords

  • Gene expression data
  • gene selection
  • nonnegative matrix factorization
  • tumor classification

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering

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