ArticleFrontiers in microbiology2024
BANNMDA: a computational model for predicting potential microbe-drug associations based on bilinear attention networks and nuclear norm minimization.
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- BRMDA: prediction model for potential microbe-drug associations based on bilinear attention networks and random forest.Frontiers in genetics · 2026Article
- BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders.Frontiers in genetics · 2025Article
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8 authors.
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Abstract
Introduction: Predicting potential associations between microbes and drugs is crucial for advancing pharmaceutical research and development. In this manuscript, we introduced an innovative computational model named BANNMDA by integrating Bilinear Attention Networks(BAN) with the Nuclear Norm Minimization (NNM) to uncover hidden connections between microbes and drugs. Methods: In BANNMDA, we initially constructed a heterogeneous microbe-drug network by combining multiple drug and microbe similarity metrics with known microbe-drug relationships. Subsequently, we applied both BAN and NNM to compute predicted scores of potential microbe-drug associations. Finally, we implemented 5-fold cross-validation frameworks to evaluate the prediction performance of BANNMDA. Results and discussion: The experimental results indicated that BANNMDA outperformed state-of-the-art competitive methods. We conducted case studies on well-known drugs such as the Amoxicillin and Ceftazidime, as well as on pathogens such as
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