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2000
Volume 20, Issue 10
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Microorganisms play a crucial role in human health and disease. Identifying potential microbe-drug associations is essential for drug discovery and clinical treatment. In this manuscript, we proposed a novel prediction model named GVNNVAE by combining an Improved Graph Neural Network (GNN) and the Variational Auto-Encoder (VAE) to infer potential microbe-drug associations. In GVNNVAE, we first established a heterogeneous microbe-drug network by integrating multiple similarity metrics of microbes, drugs, and diseases. Subsequently, we introduced an improved GNN and the VAE to extract topological and attribute representations for nodes in respectively. Finally, through incorporating various original attributes of microbes and drugs with above two kinds of newly obtained topological and attribute representations, predicted scores of potential microbe-drug associations would be calculated. Furthermore, To evaluate the prediction performance of GVNNVAE, intensive experiments were done and comparative results showed that GVNNVAE could achieve a satisfactory AUC value of 0.9688, which outperformed existing competitive state-of-the-art methods. And moreover, case studies of known microbes and drugs confirmed the effectiveness of GVNNVAE as well, which highlighted its potential for predicting latent microbe-drug associations.

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2024-10-31
2025-12-18

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GVNNVAE: A Novel Microbe-Drug Association Prediction Model based on an Improved Graph Neural Network and the Variational Auto-Encoder
Curr Bioinform 20, 930 (2025); https://doi.org/10.2174/0115748936331907240927141428
/content/journals/cbio/10.2174/0115748936331907240927141428
/content/journals/cbio/10.2174/0115748936331907240927141428
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  • Article Type:     Research Article
Keyword(s): computational models; graph neural network; heterogeneous network; Microbe-drug associations prediction; topological and attribute representations; variational auto-encoder

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