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P2‐061: Low activity of angiotensin‐converting enzyme (ACE) is a risk factor for onset of Alzheimer's disease
Author(s) -
Akatsu Hiroyasu,
Matsuyama Zenjiro,
Matsukawa Noriyuki,
Hori Akira,
Yamamoto Takayuki,
Michikawa Makoto
Publication year - 2009
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2009.04.371
Subject(s) - angiotensin converting enzyme , medicine , endocrinology , normal group , ace inhibitor , disease , enzyme assay , renin–angiotensin system , alzheimer's disease , positive correlation , enzyme , chemistry , biochemistry , blood pressure
higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from DNA microarray gene expression data. Combine with other pattern recognition methods such as hierarchical clustering methods, nonnegative matrix factorization and support vector machine, efficient sample classification and gene clustering for Alzheimer’s disease were presented. Results: We perform ICA method on hippocampal microarray gene expression data of Alzheimer’s disease (AD). Experiments results show that ICA method can improve the classification of AD samples and identify more significant genes. The identified high expression genes in AD are extracted in immunoreactions, metal protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, binding protein and ribosomal protein. And also find many significant low expression genes in above categories, and moreover, some oncogenes and phosphoricproteins are low expressed. Especially, ICA can identify more AD-related genes. Conclusions: We demonstrate that 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. Experiment results also validate that the ICA model outperforms traditional pattern recognition methods. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.