College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China.
School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
Bioinformatics. 2020 Dec 30;36(Suppl_2):i779-i786. doi: 10.1093/bioinformatics/btaa891.
Human microbes get closely involved in an extensive variety of complex human diseases and become new drug targets. In silico methods for identifying potential microbe-drug associations provide an effective complement to conventional experimental methods, which can not only benefit screening candidate compounds for drug development but also facilitate novel knowledge discovery for understanding microbe-drug interaction mechanisms. On the other hand, the recent increased availability of accumulated biomedical data for microbes and drugs provides a great opportunity for a machine learning approach to predict microbe-drug associations. We are thus highly motivated to integrate these data sources to improve prediction accuracy. In addition, it is extremely challenging to predict interactions for new drugs or new microbes, which have no existing microbe-drug associations.
In this work, we leverage various sources of biomedical information and construct multiple networks (graphs) for microbes and drugs. Then, we develop a novel ensemble framework of graph attention networks with a hierarchical attention mechanism for microbe-drug association prediction from the constructed multiple microbe-drug graphs, denoted as EGATMDA. In particular, for each input graph, we design a graph convolutional network with node-level attention to learn embeddings for nodes (i.e. microbes and drugs). To effectively aggregate node embeddings from multiple input graphs, we implement graph-level attention to learn the importance of different input graphs. Experimental results under different cross-validation settings (e.g. the setting for predicting associations for new drugs) showed that our proposed method outperformed seven state-of-the-art methods. Case studies on predicted microbe-drug associations further demonstrated the effectiveness of our proposed EGATMDA method.
Source codes and supplementary materials are available at: https://github.com/longyahui/EGATMDA/.
Supplementary data are available at Bioinformatics online.
人类微生物与广泛的复杂人类疾病密切相关，成为新的药物靶点。用于识别潜在微生物-药物关联的计算方法为传统实验方法提供了有效的补充，不仅有助于筛选候选化合物进行药物开发，还促进了微生物-药物相互作用机制的新的知识发现。另一方面，最近积累的微生物和药物的生物医学数据的可用性为机器学习方法预测微生物-药物关联提供了一个很好的机会。因此，我们非常有动力整合这些数据源以提高预测准确性。此外，预测新药物或新微生物的相互作用极具挑战性，因为这些新药物或新微生物没有现有的微生物-药物关联。
在这项工作中，我们利用各种生物医学信息源为微生物和药物构建多个网络（图）。然后，我们开发了一种新的基于图注意网络的集成框架，具有层次注意机制，用于从构建的多个微生物-药物图中预测微生物-药物关联，称为 EGATMDA。特别是，对于每个输入图，我们设计了一个具有节点级注意的图卷积网络，以学习节点（即微生物和药物）的嵌入。为了有效地从多个输入图聚合节点嵌入，我们实现了图级注意以学习不同输入图的重要性。在不同的交叉验证设置（例如，预测新药物关联的设置）下的实验结果表明，我们提出的方法优于七种最先进的方法。预测微生物-药物关联的案例研究进一步证明了我们提出的 EGATMDA 方法的有效性。
源代码和补充材料可在 https://github.com/longyahui/EGATMDA/ 获得。
补充数据可在生物信息学在线获得。
