Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early intervention.

Methods

Here, we used tree-based and deep learning methods to train polyomic prediction models for AD affection status and age at onset, employing genomic, proteomic, metabolomic, and drug use data from UK Biobank. We used SHAP to determine the feature's importance.

Results

Our best-performing polyomic model achieved an area under the receiver operating characteristics curve (AUROC) of 0.87. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides apolipoprotein E (APOE) alleles. Inc

SUBMITTER: Ngai T 

PROVIDER: S-EPMC11667526 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature