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Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network
Jia Qu1, Jie Ni1, Tong-Guang Ni1
1School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University, Changzhou, 213164, China.
Current Medicinal Chemistry
|September 3, 2024
Summary
This study introduces a novel graph convolutional network model for predicting microbe-drug associations, crucial for drug development and combating resistance. The model demonstrates high accuracy, offering a faster alternative to traditional methods.
Area of Science:
- Microbiology and Pharmacology
- Computational Biology and Bioinformatics
- Genomics and Drug Discovery
Background:
- Human microbes are linked to complex diseases and are emerging as critical drug targets.
- Identifying microbe-drug associations is vital for drug development, precision medicine, and addressing antimicrobial resistance.
Purpose of the Study:
- To propose a novel computational model for predicting microbe-drug associations.
- To leverage a layer attention graph convolutional network for enhanced prediction accuracy.
Main Methods:
- Integrated multiple biological data into a heterogeneous network.
- Employed a graph convolutional network (GCN) to embed microbes and drugs.
- Utilized a layer attention mechanism to decode embeddings and calculate association scores.
Main Results:
- Achieved high Area Under the Curve (AUC) values, e.g., 0.9178 on aBiofilm dataset using global LOOCV.
- Demonstrated strong performance in 5-fold cross-validation with average AUCs of 0.9141 and 0.8982 for aBiofilm and MDAD datasets, respectively.
- Case studies further validated the model's predictive capabilities.
Conclusions:
- The proposed computational model offers an efficient alternative to time-consuming traditional methods for microbe-drug association prediction.
- The model shows significant potential for advancing drug development and precision medicine.
- Accurate prediction of microbe-drug associations can aid in developing new therapies and combating drug resistance.
