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

• URL: http://arxiv.org/abs/2508.07465v1
• Date: Sun, 10 Aug 2025 19:35:53 GMT
• Title: MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification
• Authors: Tiantian Yang, Zhiqian Chen,
• Abstract summary: We propose Multi-Omics integration with Tree-generated Graph Neural Network (MOTGNN), a novel and interpretable framework for binary disease classification.<n>MOTGNN employs eXtreme Gradient Boosting (XGBoost) to perform 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.
• Score: 8.939868953031976
• License: http://creativecommons.org/licenses/by/4.0/
• 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 and complex interactions among omics layers present major challenges for predictive modeling. 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) to perform 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. On 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 (e.g., 87.2% vs. 33.4% F1 on imbalanced data). The model maintains computational efficiency through sparse graphs (2.1-2.8 edges per node) and provides built-in interpretability, revealing both top-ranked biomarkers and the relative contributions of each omics modality. These results highlight MOTGNN's potential to improve both predictive accuracy and interpretability in multi-omics disease modeling.

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