MDSVDNV: predicting microbe–drug associations by singular value decomposition and Node2vec - Tan, Huilin; Zhang, Zhen; Liu, Xin; Chen, Yiming; Yang, Zinuo; Wang, Lei (Frontiers in Microbiology, 2024)
According to recent researches, microbes are crucial for the growth and development of the human body, the movement of nutrients and human health. Diseases may arise as a result of disruptions and imbalances of the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes. In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which, the Node2vec network embedding approach and the Singular Value Decomposition (SVD) matrix decomposition method were first adopted to produce linear and nonlinear representations of microorganisms and medications respectively. And then, we would combine these two kinds of representations to an integrated representation for each microbe and drug, and input these integrated representations into the XGBoost, a machine learning classifier, to eventually obtain the anticipated scores of possible drug-microbe interactions. Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under the 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.