LD-informed deep learning for Alzheimer's gene loci detection using WGS data.

ABSTRACT:

Introduction

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.

Methods</

SUBMITTER: Jo T 

PROVIDER: S-EPMC11736638 | biostudies-literature | 2025 Jan-Mar

REPOSITORIES: biostudies-literature