ArticleBMC bioinformatics2022
GSAMDA: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- BRMDA: prediction model for potential microbe-drug associations based on bilinear attention networks and random forest.Frontiers in genetics · 2026Article
- Computational prediction and conformation of relationships among microbes, drugs and diseases.Scientific reports · 2025Article
- Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes.BMC bioinformatics · 2025Article
- KGCLMDA: a computational model for predicting latent associations of microbial drugs using knowledge graphs and contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- CLMT: graph contrastive learning model for microbe-drug associations prediction with transformer.Frontiers in genetics · 2025Article
- GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features.Scientific reports · 2024Article
- Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.Bioinformatics (Oxford, England) · 2024Article
- A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest.BMC bioinformatics · 2024Article
- Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.Bioinformatics (Oxford, England) · 2024Article
- NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network.Scientific reports · 2024Article
- BANNMDA: a computational model for predicting potential microbe-drug associations based on bilinear attention networks and nuclear norm minimization.Frontiers in microbiology · 2024Article
- A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders.Frontiers in microbiology · 2024Article
- Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest.Frontiers in microbiology · 2024Article
- LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble.Frontiers in microbiology · 2024Article
- OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks.Frontiers in genetics · 2024Article
- A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism.Scientific reports · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe-drug associations have been proposed.
resultsIn this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe-drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe-drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe-drug pairs.
conclusionA novel computational model is proposed for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.