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.

翻译：整合DNA甲基化、mRNA表达和microRNA（miRNA）表达等多组学数据，为理解疾病背后的生物学机制提供了全面视角。然而，多组学数据的高维性、模态间的异质性以及可靠生物相互作用网络的缺乏，使得有意义的整合面临挑战。此外，现有许多模型依赖人工构建的相似性图、易受类别不平衡影响，且往往缺乏内置可解释性，限制了其在生物医学应用中的实用性。我们提出一种新颖且可解释的二元疾病分类框架——基于树生成图神经网络的多组学整合方法（MOTGNN）。MOTGNN采用极限梯度提升（XGBoost）进行组学特异性监督图构建，随后通过模态专用图神经网络（GNNs）进行分层表征学习，并利用深度前馈网络实现跨组学整合。在三个真实世界疾病数据集上的实验表明，MOTGNN在准确率、ROC-AUC和F1分数上均优于当前最先进的基线方法5-10%，且对严重的类别不平衡保持稳健。该模型通过使用稀疏图保持计算效率，并提供内置可解释性，既能揭示排名靠前的生物标志物，也能解析各组学模态的相对贡献。这些结果凸显了MOTGNN在提升多组学疾病建模的预测准确性与可解释性方面的潜力。