ATOMIC: A graph attention neural network for ATOpic dermatitis prediction on human gut MICrobiome

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 node features, 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). As a result, ATOMIC achieved an AUROC of 0.810 and an AUPRC of 0.927 on the KNUH dataset. Furthermore, ATOMIC identified microbes potentially associated with AD prediction and proposed candidate microbial biomarkers that may inform future therapeutic strategies. 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. Author summaryAtopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 120 million people worldwide, often leading to a reduced quality of life, including sleep disturbances and stress. Current treatment methods rarely achieve complete remission, and the precise cause of AD remains unclear. The association between imbalances in the gut microbiota and the pathogenesis of AD has highlighted the gut microbiota as a promising target for therapeutic strategies. Exploring the potential role of the gut microbiota in modulating host immune responses is likely to have a positive impact on future AD treatment. Consequently, the growing need to develop machine learning and deep learning-based predictive models using gut microbiome data. However, existing models often suffer from limited interpretability owing to their black-box nature and frequently overlook microbial interactions and genomic contexts. To address these limitations, we propose a novel model called ATOMIC, which integrates microbial co-expression networks and genomic information to predict AD. Our model achieved superior performance in terms of AUROC, AUPRC, and F1 scores compared with existing models and provided interpretable insights through an attention mechanism, thereby offering promising potential for identifying relevant biomarkers. Together, these results highlight ATOMICs potential for the accurate prediction of AD and for identifying biologically meaningful microbial signatures.