Abstract
Background:Alzheimer’s disease (AD) is the most common form of senile dementia. However, its pathological mechanisms are not fully understood. In order to comprehend AD pathological mechanisms, researchers employed AD-related DNA microarray data and diverse computational algorithms. More efficient computational algorithms are needed to process DNA microarray data for identifying AD-related candidate genes.Methods:In this paper, we propose a specific algorithm that is based on the following observation: When an acrobat walks along a steel-wire, his/her body must have some swing; if the swing can be controlled, then the acrobat can maintain the body balance. Otherwise, the acrobat will fall. Based on this simple idea, we have designed a simple, yet practical, algorithm termed as the Amplitude Deviation Algorithm (ADA). Deviation, overall deviation, deviation amplitude, and 3δ are introduced to characterize ADA.Results:52 candidate genes for AD have been identified via ADA. The implications for some of the AD candidate genes in AD pathogenesis have been discussed.Conclusions:Through the analysis of these AD candidate genes, we believe that AD pathogenesis may be related to the abnormality of signal transduction (AGTR1 and PTAFR), the decrease in protein transport capacity (COL5A2 (221729_at), COL5A2 (221730_at), COL4A1), the impairment of axon repair (CNR1), and the intracellular calcium dyshomeostasis (CACNB2, CACNA1E). However, their potential implication for AD pathology should be further validated by wet lab experiments as they were only identified by computation using ADA.
Highlights
Introduction ofDNA microarray and its application on Alzheimer’s disease (AD) genes With little known about the cause of AD, it is necessary to identify more AD-related candidate genes
It is worth noting that the 52 genes discovered with the Amplitude Deviation Algorithm (ADA) contain formerly identified AD candidate genes
The abnormal expression of gene CNR1 correlates with the absence of LTD, which may lead to the impairment of LTP and may induce AD
Summary
DNA microarray and its application on AD genes With little known about the cause of AD, it is necessary to identify more AD-related candidate genes. In order to comprehend AD pathological mechanisms, researchers employed AD-related DNA microarray data and diverse computational algorithms. More efficient computational algorithms are needed to process DNA microarray data for identifying AD-related candidate genes. It is of common opinion that AD is related to amyloid plaques (Aβ) and neurofibrillary tangles (NFT) in the brain [1,2]. This hypothesis is supported by the location of the AD causative gene called the amyloid precursor protein (APP) on chromosome 21 [5,6]. In 2009, this theory was updated, suggesting that a close relative of the beta-amyloid protein, and not necessarily the betaamyloid itself, may be the major culprit of the disease [7]
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