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.

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).

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.

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.