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.

Assessment of gene order computing methods for Alzheimer's disease.

BACKGROUND: Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. METHODS: Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. RESULTS: Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. CONCLUSION: Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods.

Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm.

As Alzheimer's Disease (AD) is the most common form of dementia, the study of AD-related genes via biocomputation is an important research topic. One method of studying AD-related gene is to cluster similar genes together into a gene order. Gene order is a good clustering method as the results can be optimal globally while other clustering methods are only optimal locally. Herein we use the Ant Colony Optimisation (ACO)-based algorithm to calculate the gene order from an Alzheimer's DNA microarray dataset. We test it with four distance measurements: Pearson distance, Spearmen distance, Euclidean distance, and squared Euclidean distance. Our computing results indicate: a different distance formula generated a different quality of gene order, the squared Euclidean distance approach produced the optimal AD-related gene order.

A special local clustering algorithm for identifying the genes associated with Alzheimer's disease.

Clustering is the grouping of similar objects into a class. Local clustering feature refers to the phenomenon whereby one group of data is separated from another, and the data from these different groups are clustered locally. A compact class is defined as one cluster in which all similar elements cluster tightly within the cluster. Herein, the essence of the local clustering feature, revealed by mathematical manipulation, results in a novel clustering algorithm termed as the special local clustering (SLC) algorithm that was used to process gene microarray data related to Alzheimer's disease (AD). SLC algorithm was able to group together genes with similar expression patterns and identify significantly varied gene expression values as isolated points. If a gene belongs to a compact class in control data and appears as an isolated point in incipient, moderate and/or severe AD gene microarray data, this gene is possibly associated with AD. Application of a clustering algorithm in disease-associated gene identification such as in AD is rarely reported.