Identification and Analysis of Alzheimer's Candidate Genes by an Amplitude Deviation Algorithm.

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.