Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest

• Published in: Frontiers in microbiology Vol. 15; p. 1394302
• Main Authors: Wang, Bo, Ma, Fangjian, Du, Xiaoxin, Zhang, Guangda, Li, Jingyou
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 31.05.2024
• Subjects: computational prediction model; graph attention network; microbe–drug association prediction; Microbiology; modified graph convolutional neural network; random forest classifier; variational autoencoder; graph attention network; computational prediction model; microbe–drug association prediction; random forest classifier; variational autoencoder; modified graph convolutional neural network
• DOI: 10.3389/fmicb.2024.1394302
• ISSN: 1664-302X

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. 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. 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. 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.