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

No Thumbnail Available

Date

2022

Journal Title

Journal ISSN

Volume Title

Publisher

Mdpi

Research Projects

Organizational Units

Organizational Unit

Journal Issue

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 analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection.

Description

Sánchez-Pérez, Ana María M/0000-0002-5811-0005; Inal Gultekin, Guldal/0000-0002-8313-6119; Sanchez-Perez, Ana Maria María/0000-0002-5811-0005; KALKAN, Habil/0000-0002-5456-8385; AKKAYA, UMIT MURAT/0000-0002-5247-4860

Keywords

dementia, mild cognitive impairment, local discriminant analysis, deep learning

Turkish CoHE Thesis Center URL

Citation

1

WoS Q

Q2

Scopus Q

Q2

Source

Volume

13

Issue

8

Start Page

End Page