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Volume 19, Issue 10
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Introduction

Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships through traditional wet lab approaches.

Methods

We proposed an efficient computational model, STNMDA, that integrated a Structure-Aware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbe-drug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated the probability of associations between microbes and drugs.

Results

Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations.

Conclusion

Hence, STNMDA showed promise as a valuable tool for future prediction of microbe-drug associations.

© 2024 Bentham Science Publishers
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2024-02-02
2024-11-22

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STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer
Curr Bioinform 19, 919 (2024); https://doi.org/10.2174/0115748936272939231212102627
/content/journals/cbio/10.2174/0115748936272939231212102627
/content/journals/cbio/10.2174/0115748936272939231212102627
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  • Article Type:     Research Article
Keyword(s): bile acids; biomarkers; deep neural network; microbe-disease-drug association; Microbe-drug association; structure-aware transformer

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