Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout

TL;DR

This paper introduces a transformer-based ensemble model with uncertainty estimation for classifying Alzheimer's disease from genomic data, significantly improving accuracy by filtering uncertain cases.

Contribution

It presents a novel ensemble approach combining transformers and random forests with Monte Carlo Dropout for uncertainty-aware genomic classification of AD.

Findings

Accuracy improved from 62.63% to 72.87% when excluding uncertain samples

Uncertainty estimation effectively identifies ambiguous cases for further clinical review

Model achieves an AUC of 0.6636 on test data

Abstract

INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into an uncertain (high-variance) group and a more certain (low-variance) group. RESULTS: We analyzed 1050 individuals, holding out half for testing. Overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC) were 0.6514 and 0.6636, respectively. Excluding the uncertain group improved accuracy from 0.6263 to 0.7287 (10.24% increase) and F1 from 0.5843 to 0.8205 (23.62% increase). DISCUSSION: Monte Carlo Dropout-driven uncertainty helps identify ambiguous cases that may require further clinical evaluation, thus improving reliability in AD genomic classification.