ATOMIC: a graph attention network for atopic dermatitis prediction using human gut microbiome

TL;DR

This paper introduces ATOMIC, a machine learning model that uses gut microbiome data to predict atopic dermatitis and identify potential microbial biomarkers.

Contribution

ATOMIC is a novel graph attention network that incorporates microbial co-expression and genomic data for interpretable AD prediction.

Findings

ATOMIC achieved an AUROC of 0.810 and AUPRC of 0.927 on the KNUH dataset.

The model identified microbes potentially associated with AD and candidate biomarkers.

A gut microbial abundance dataset and model code were publicly released for future research.

Abstract

Atopic dermatitis (AD) is a chronic inflammatory skin disease driven by complex interactions among genetic, environmental, and microbial factors; however, its etiology remains unclear. Recent studies have reported the role of gut microbiota dysbiosis in AD pathogenesis, leading to increased interest in microbiome-targeted therapeutic strategies such as probiotics and fecal microbiota transplantation. Building on these findings, recent advances in computational modeling have introduced machine learning and deep learning-based approaches to capture the nonlinear relationships between gut microbiota and diseases. However, these models focus on diseases other than AD and often fail to capture complex microbial interactions or incorporate microbial genomic information, thereby offering limited interpretability.

To address these limitations, we propose ATOMIC, an interpretable graph attention network-based model that incorporates microbial co-expression networks to predict AD. Microbial co-expression networks incorporate microbial genomic information as a node feature, thereby enhancing their ability to capture functionally relevant microbial patterns. To train and test our model, we collected and processed 99 gut microbiome samples from adult patients with AD and healthy controls at Kangwon National University Hospital (KNUH).

ATOMIC outperformed baseline models, achieving an AUROC of 0.810 and an AUPRC of 0.927 for KNUH dataset. Furthermore, ATOMIC identified microbes potentially associated with AD prediction and proposed candidate microbial biomarkers that may inform future therapeutic strategies.

By identifying key microbial taxa that contributed to the AD classification through its interpretable attention mechanism, ATOMIC provides a foundation for personalized microbiome-based interventions and biomarker discovery. Additionally, to facilitate future research, we publicly released a gut microbial abundance dataset from KNUH. The source code and processed abundance data are available from ATOMIC GitHub repository at https://www.github.com/KU-MedAI/ATOMIC.

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