Title:STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer
Volume: 19
Issue: 10
Author(s): Liu Fan, Xiaoyu Yang, Lei Wang*Xianyou Zhu*
Affiliation:
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China
- College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421010, China
Keywords:
Microbe-drug association, microbe-disease-drug association, structure-aware transformer, deep neural network, biomarkers, bile acids.
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 microbedrug
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 microbedrug
associations.
