Prediction of Alzheimer's Disease by a Novel Image-Based Representation of Gene Expression.

ABSTRACT: Early intervention can delay the progress of Alzheimer's Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes' expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analy

SUBMITTER: Kalkan H 

PROVIDER: S-EPMC9407775 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature