human microbes play a critical role in an extensive range of complex human diseases and become a new target in precision medicine. In silico methods of identifying microbe–disease associations not only can provide a deep insight into understanding the pathogenic mechanism of complex human diseases but also assist pharmacologists to screen candidate targets for drug development. However, the majority of existing approaches are based on linear models or label propagation, which suffers from limitations in capturing nonlinear associations between microbes and diseases. Besides, it is still a great challenge for most previous methods to make predictions for new diseases (or new microbes) with few or without any observed associations.

中文翻译：

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通过具有归纳矩阵完成的图注意力网络预测人类微生物-疾病关联。

人类微生物在广泛的复杂人类疾病中发挥着关键作用，成为精准医学的新靶点。识别微生物与疾病关联的计算机方法不仅可以深入了解人类复杂疾病的发病机制，还可以帮助药理学家筛选药物开发的候选靶点。然而，大多数现有方法都基于线性模型或标签传播，在捕捉微生物和疾病之间的非线性关联方面存在局限性。此外，对于大多数以前的方法来说，在几乎没有或没有任何观察到的关联的情况下对新疾病（或新微生物）进行预测仍然是一个巨大的挑战。