1. GCGACNN: A Graph Neural Network and Random Forest for Predicting Microbe–Drug Associations
- Author
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Shujuan Su, Meiling Liu, Jiyun Zhou, and Jingfeng Zhang
- Subjects
graph convolutional network ,graph attention network ,two-dimensional convolutional neural network ,microbiome–drug associations ,Microbiology ,QR1-502 - Abstract
The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of drug resistance genes, and the impact of microbial communities on host drug metabolism and immune modulation. These interactions significantly impact drug efficacy and the evolution of drug resistance. In this study, we propose a novel predictive model, termed GCGACNN. We first collected microbe, disease, and drug association data from multiple databases and the relevant literature to construct three association matrices and generate similarity feature matrices using Gaussian similarity functions. These association and similarity feature matrices were then input into a multi-layer Graph Neural Network for feature extraction, followed by a two-dimensional Convolutional Neural Network for feature fusion, ultimately establishing an effective predictive framework. Experimental results demonstrate that GCGACNN outperforms existing methods in predictive performance.
- Published
- 2024
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