Visualization and classification of microarray gene data by nonnegtive matrix factorization

Wei Kong, Xiaoyang Mou, Weijie Tao, Yao Xia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gene microarray technology is an effective tool to collect the expression levels of thousands of genes from a single array. However, exploitation of the huge amount of data generated by microarrays is difficult because they are complex and noisy high-dimensional data. In this work, we present a biclustering method nonnegtive matrix factorization (NMF) to reduce the dimensionality of the data and discover the underlying biological process from gene expression data of Alzheimer's disease (AD). The simulation results show that the reduction of dimension and identification of informatively biological process are useful for both visualization and analyzing of such high-throughput gene dataset.

Original languageEnglish (US)
Title of host publicationISPACS 2010 - 2010 International Symposium on Intelligent Signal Processing and Communication Systems, Proceedings
DOIs
StatePublished - Dec 1 2010
Event18th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2010 - Chengdu, China
Duration: Dec 6 2010Dec 8 2010

Publication series

NameISPACS 2010 - 2010 International Symposium on Intelligent Signal Processing and Communication Systems, Proceedings

Other

Other18th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2010
Country/TerritoryChina
CityChengdu
Period12/6/1012/8/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Signal Processing
  • Communication

Fingerprint

Dive into the research topics of 'Visualization and classification of microarray gene data by nonnegtive matrix factorization'. Together they form a unique fingerprint.

Cite this