MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification

TL;DR

MOTGNN is an interpretable multi-omics graph neural network framework that improves disease classification accuracy, robustness, and biomarker interpretability across real-world datasets.

Contribution

It introduces a novel framework combining XGBoost-based graph construction with GNNs for hierarchical multi-omics integration and interpretability.

Findings

Outperforms state-of-the-art models by 5-10% in accuracy, ROC-AUC, and F1-score.

Remains robust under severe class imbalance.

Provides insights into key biomarkers and modality contributions.

Abstract

Integrating multi-omics data, such as DNA methylation, mRNA expression, and microRNA (miRNA) expression, offers a comprehensive view of the biological mechanisms underlying disease. However, the high dimensionality of multi-omics data, the heterogeneity across modalities, and the lack of reliable biological interaction networks make meaningful integration challenging. In addition, many existing models rely on handcrafted similarity graphs, are vulnerable to class imbalance, and often lack built-in interpretability, limiting their usefulness in biomedical applications. We propose Multi-Omics integration with Tree-generated Graph Neural Network (MOTGNN), a novel and interpretable framework for binary disease classification. MOTGNN employs eXtreme Gradient Boosting (XGBoost) for omics-specific supervised graph construction, followed by modality-specific Graph Neural Networks (GNNs) for hierarchical representation learning, and a deep feedforward network for cross-omics integration. Across three real-world disease datasets, MOTGNN outperforms state-of-the-art baselines by 5-10% in accuracy, ROC-AUC, and F1-score, and remains robust to severe class imbalance. The model maintains computational efficiency through the use of sparse graphs and provides built-in interpretability, revealing both top-ranked biomarkers and the relative contributions of each omics modality. These results highlight the potential of MOTGNN to improve both predictive accuracy and interpretability in multi-omics disease modeling.