PDF(Pubmed)
Abstract：
UNASSIGNED: The identification of microbe-drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them ignore the combination of abundant sequence, structural information, and microbe-drug network topology.
UNASSIGNED: In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe-drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features.
UNASSIGNED: The experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively.
UNASSIGNED: We conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations.
UNASSIGNED: In this study, we developed a computational framework based on a modified graph attention variational autoencoder (MGAVAEMDA) to infer potential microbedrug associations by combining biological information with the variational autoencoder. In MGAVAEMDA, we first used multiple databases, which include microbial sequences, drug structures, and microbe-drug association databases, to establish two comprehensive feature matrices of microbes and drugs after multiple similarity computations, fusion, smoothing, and thresholding. Then, we employed a combination of variational autoencoder and graph attention to extract low-dimensional feature representations of microbes and drugs. Finally, the lowdimensional feature representation and graphical adjacency matrix were input into the random forest classifier to obtain the microbe-drug association score to identify the potential microbe-drug association. Moreover, in order to correct the model complexity and redundant calculation to improve efficiency, we introduced a modified graph convolutional neural network embedded into the variational autoencoder for computing low dimensional features.
UNASSIGNED: The experiment results demonstrate that the prediction performance of MGAVAEMDA is better than the five state-of-the-art methods. For the major measurements (AUC =0.9357, AUPR =0.9378), the relative improvements of MGAVAEMDA compared to the suboptimal methods are 1.76 and 1.47%, respectively.
UNASSIGNED: We conducted case studies on two drugs and found that more than 85% of the predicted associations have been reported in PubMed. The comprehensive experimental results validated the reliability of our models in accurately inferring potential microbe-drug associations.
摘要：
■微生物-药物关联的识别可以极大地促进药物研发。用于筛选微生物-药物关联的传统方法是耗时的,人力密集型，而且行为成本很高，所以计算方法是一个很好的选择。然而,他们中的大多数忽略了丰富序列的组合，结构信息，和微生物-药物网络拓扑。
■在这项研究中，我们开发了一个基于改进型图注意力变分自编码器(MGAVAEMDA)的计算框架,通过将生物信息与变分自编码器相结合来推断潜在的微药物关联.在MGAVAEMDA,我们首先使用了多个数据库，其中包括微生物序列，药物结构，和微生物-药物关联数据库，经过多次相似度计算，建立微生物和药物的两个综合特征矩阵，聚变,平滑，和阈值。然后，我们采用了变分自动编码器和图形注意力的组合来提取微生物和药物的低维特征表示。最后,将低维特征表示和图形邻接矩阵输入随机森林分类器,以获得微生物-药物关联评分,从而识别潜在的微生物-药物关联.此外，为了校正模型复杂性和冗余计算以提高效率，我们引入了一个改进的图卷积神经网络嵌入到变分自动编码器用于计算低维特征。
■实验结果表明，MGAVAEMDA的预测性能优于五种最先进的方法。对于主要测量（AUC=0.9357，AUPR=0.9378），与次优方法相比，MGAVAEMDA的相对改进分别为1.76%和1.47%，分别。
■我们对两种药物进行了案例研究，发现PubMed中已报道了超过85％的预测关联。综合实验结果验证了我们模型在准确推断潜在微生物-药物关联方面的可靠性。
■在这项研究中，我们开发了一个基于改进型图注意力变分自编码器(MGAVAEMDA)的计算框架,通过将生物信息与变分自编码器相结合来推断潜在的微药物关联.在MGAVAEMDA,我们首先使用了多个数据库，其中包括微生物序列，药物结构，和微生物-药物关联数据库，经过多次相似度计算，建立微生物和药物的两个综合特征矩阵，聚变,平滑，和阈值。然后，我们采用了变分自动编码器和图形注意力的组合来提取微生物和药物的低维特征表示。最后,将低维特征表示和图形邻接矩阵输入随机森林分类器,以获得微生物-药物关联评分,从而识别潜在的微生物-药物关联.此外，为了校正模型复杂性和冗余计算以提高效率，我们引入了一个改进的图卷积神经网络嵌入到变分自动编码器用于计算低维特征。
■实验结果表明，MGAVAEMDA的预测性能优于五种最先进的方法。对于主要测量（AUC=0.9357，AUPR=0.9378），与次优方法相比，MGAVAEMDA的相对改进分别为1.76%和1.47%，分别。
■我们对两种药物进行了案例研究，发现PubMed中已报道了超过85％的预测关联。综合实验结果验证了我们模型在准确推断潜在微生物-药物关联方面的可靠性。
