School of Software and Information Engineering, Hunan Software Vocational and Technical University, Xiangtan, 411108, China.
Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China.
BMC Bioinformatics. 2023 Feb 2;24(1):35. doi: 10.1186/s12859-023-05158-7.
As new drug targets, human microbes are proven to be closely related to human health. Effective computational methods for inferring potential microbe-drug associations can provide a useful complement to conventional experimental methods and will facilitate drug research and development. However, it is still a challenging work to predict potential interactions for new microbes or new drugs, since the number of known microbe-drug associations is very limited at present. In this manuscript, we first constructed two heterogeneous microbe-drug networks based on multiple measures of similarity of microbes and drugs, and known microbe-drug associations or known microbe-disease-drug associations, respectively. And then, we established two feature matrices for microbes and drugs through concatenating various attributes of microbes and drugs. Thereafter, after taking these two feature matrices and two heterogeneous microbe-drug networks as inputs of a two-layer graph attention network, we obtained low dimensional feature representations for microbes and drugs separately. Finally, through integrating low dimensional feature representations with two feature matrices to form the inputs of a convolutional neural network respectively, a novel computational model named GACNNMDA was designed to predict possible scores of microbe-drug pairs. Experimental results show that the predictive performance of GACNNMDA is superior to existing advanced methods. Furthermore, case studies on well-known microbes and drugs demonstrate the effectiveness of GACNNMDA as well. Source codes and supplementary materials are available at: https://github.com/tyqGitHub/TYQ/tree/master/GACNNMDA.
作为新的药物靶点，人类微生物被证明与人类健康密切相关。有效的计算方法可以推断出潜在的微生物-药物关联，为传统的实验方法提供了有用的补充，并将促进药物的研发。然而，对于新的微生物或新的药物，预测其潜在的相互作用仍然是一项具有挑战性的工作，因为目前已知的微生物-药物关联非常有限。在本文中，我们首先分别基于微生物和药物的相似性的多种度量和已知的微生物-药物关联或已知的微生物-疾病-药物关联，构建了两个异构的微生物-药物网络。然后，我们通过串联微生物和药物的各种属性，为微生物和药物分别建立了两个特征矩阵。之后，我们将这两个特征矩阵和两个异构的微生物-药物网络作为一个两层图注意力网络的输入，分别为微生物和药物获得低维特征表示。最后，通过将低维特征表示与两个特征矩阵集成到形成卷积神经网络的输入中，设计了一个名为 GACNNMDA 的新型计算模型来预测微生物-药物对的可能评分。实验结果表明，GACNNMDA 的预测性能优于现有的先进方法。此外，对著名的微生物和药物的案例研究也证明了 GACNNMDA 的有效性。源代码和补充材料可在：https://github.com/tyqGitHub/TYQ/tree/master/GACNNMDA。
