ArticleFrontiers in microbiology2023
MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 14 citations in OpenAlex.
- HAFMMDA: HIN2vec-Based Attentional Factorization Machines for Predicting Microbe-Drug Associations.Interdisciplinary sciences, computational life sciences · 2025Article
- KGCLMDA: a computational model for predicting latent associations of microbial drugs using knowledge graphs and contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.Briefings in bioinformatics · 2025Article
- GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.Scientific reports · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
Introduction: Recent researches have demonstrated that 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 in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes. Methods: 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 non-linear representations of microbe interactions. Results and discussion: Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 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.
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