Study DNA microarray gene expression data of Alzheimer's disease by independent component analysis

Wei Kong, Xiaoyang Mou, Bin Yang

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

2 Scopus citations

Abstract

Rapid progress in deciphering the biological mechanism of Alzheimer's disease (AD) has arisen from the application of molecular and cell biology to this complex disorder of the limbic and association cortices. The precise diagnosis of AD, however, has little progress and is also a challenging task. In this study, we investigate the DNA gene expression data of AD based on independent component analysis (ICA) to find significant genes for AD diagnosis. ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that may be interpreted as potential regulation pathways. This method can identify many genes and related pathways that play a prominent role in AD and relate the activation patterns of these to AD phenotypes. Using the extracted significant genes, the classification of AD and control samples gets more easy and effective by less gene data. This report shows that ICA as a microarray data analysis tool could help us to understand the phenotype-pathway relationship and, thus will help us to elucidate the molecular taxonomy of AD.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009
Pages44-47
Number of pages4
DOIs
StatePublished - Nov 26 2009
Event2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009 - Shanghai, China
Duration: Aug 3 2009Aug 5 2009

Publication series

NameProceedings - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009

Other

Other2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009
CountryChina
CityShanghai
Period8/3/098/5/09

All Science Journal Classification (ASJC) codes

  • Software
  • Biomedical Engineering

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