Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies. STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git .

中文翻译：

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具有拓扑正则化的多模态网络深度学习用于药物重新定位


药物疾病预测的计算技术对于增强药物发现和重新定位至关重要。虽然许多方法利用来自各种生物数据库的多模式网络，但很少整合全面的多组学数据，包括转录组、蛋白质组和代谢组。我们引入了 STRGNN，这是一种新颖的图深度学习方法，它使用包含蛋白质、RNA、代谢物和化合物的广泛多模态网络来预测药物与疾病的关系。我们构建了一个包含多组学数据的详细数据集，并开发了一种具有拓扑正则化的学习算法。该算法有选择地利用信息模式，同时过滤掉冗余。与现有方法相比，STRGNN 表现出更高的准确性，并确定了几种新的药物作用，证实了现有文献。 STRGNN 成为药物预测和发现的强大工具。 STRGNN 的源代码以及用于性能评估的数据集可在 https://github.com/yuto-ohnuki/STRGNN.git 上获取。