Du Xiaoxin, Li Jingwei, Wang Bo, Zhang Jianfei, Wang Tongxuan, Wang Junqi
Computer and Control Engineering College, Qiqihar University, Qiqihar, 161006, China.
Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, 161006, China.
Interdiscip Sci. 2025 Jun;17(2):344-358. doi: 10.1007/s12539-024-00678-z. Epub 2025 Jan 7.
The process of discovering new drugs related to microbes through traditional biological methods is lengthy and costly. In response to these issues, a new computational model (NRGCNMDA) is proposed to predict microbe-drug associations. First, Node2vec is used to extract potential associations between microorganisms and drugs, and a heterogeneous network of microbes and drugs is constructed. Then, a Graph Convolutional Network incorporating a fusion residual network mechanism (REGCN) is utilized to learn meaningful high-order similarity features. In addition, conditional random fields (CRF) are applied to ensure that microbes and drugs have similar feature embeddings. Finally, unobserved microbe-drug associations are scored based on combined embeddings. The experimental findings demonstrate that the NRGCNMDA approach outperforms several existing deep learning methods, and its AUC and AUPR values are 95.16% and 93.02%, respectively. The case study demonstrates that NRGCNMDA accurately predicts drugs associated with Enterococcus faecalis and Listeria monocytogenes, as well as microbes associated with ibuprofen and tetracycline.
通过传统生物学方法发现与微生物相关的新药的过程漫长且成本高昂。针对这些问题，提出了一种新的计算模型（NRGCNMDA）来预测微生物-药物关联。首先，使用Node2vec提取微生物与药物之间的潜在关联，并构建微生物和药物的异质网络。然后，利用结合了融合残差网络机制的图卷积网络（REGCN）来学习有意义的高阶相似性特征。此外，应用条件随机场（CRF）来确保微生物和药物具有相似的特征嵌入。最后，基于组合嵌入对未观察到的微生物-药物关联进行评分。实验结果表明，NRGCNMDA方法优于几种现有的深度学习方法，其AUC和AUPR值分别为95.16%和93.02%。案例研究表明，NRGCNMDA能够准确预测与粪肠球菌和单核细胞增生李斯特菌相关的药物，以及与布洛芬和四环素相关的微生物。
