NDLI: Study DNA Microarray Gene Expression Data of Alzheimer's Disease by Independent Component Analysis

Content Provider	IEEE Xplore Digital Library
Author	Wei Kong Xiaoyang Mou Bin Yang
Copyright Year	2009
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.
Starting Page	44
Ending Page	47
File Size	1079013
Page Count	4
File Format	PDF
ISBN	9780769537399
DOI	10.1109/IJCBS.2009.106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Data analysis #NAME? DNA Independent component analysis Educational institutions Gene expression Data mining Alzheimer's disease Cancer Sensor arrays
Content Type	Text
Resource Type	Article

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