Identification of Genes Involved in the Early Stages of Alzheimer Disease Using a Neural Network Algorithm

Abstract

Alzheimer disease is one form of dementia in old age. Alzheimer disease, the incurable disease, which is usually in the seventh decade of human life, shows its symptoms. The disease may be present for years without clinical symptoms. The current study identified the genes with altered expression in patients with Alzheimer disease. The important sequence of each gene in Alzheimer disease was found and introduced as a biomarker of this disease. The present study used microarray libraries related to Alzheimer disease. Finally, the data were weighted using 10 data mining methods, including methods such as support vector machine (SVM), deviation, information gain ratio and the Gini coefficient. Sequences with least two algorithm weights above 0.5 were selected as the most important sequences. Then, a neural network algorithm (neural net, auto multilayer perceptron and perceptron) was run on 11 data bases from the weighted perceptron algorithm, resulting in a careful 97% best performance.