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WSGMB: weight signed graph neural network for microbial biomarker identification.
Shuheng Pan1, Xinyi Jiang1, Kai Zhang1
1Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518005, China.
Briefings in Bioinformatics
|December 12, 2023
Summary
This study introduces WSGMB, a computational framework for identifying gut microbial biomarkers. WSGMB utilizes a weighted signed graph neural network to accurately pinpoint microbes critical for distinguishing between health and disease states.
Area of Science:
- Microbiology
- Computational Biology
- Bioinformatics
Background:
- Gut microbiota stability is crucial for human health.
- Dysbiosis is linked to various diseases, with specific microbes acting as potential biomarkers.
- Accurate identification of microbial biomarkers improves clinical decision-making.
Purpose of the Study:
- To introduce WSGMB, a novel computational framework for enhanced microbial biomarker identification.
- To improve the accuracy and reliability of identifying disease-related gut microbes.
- To leverage graph convolutional neural networks for analyzing microbial co-occurrence patterns.
Main Methods:
- WSGMB treats microbial co-occurrence networks as weighted signed graphs.
- Employs graph convolutional neural network techniques for graph classification.
- Designs a novel architecture to compute microbial role transitions between health and disease networks.
Main Results:
- The weighted signed graph neural network improves graph embedding quality.
- Quantifying microbial importance in co-occurrence networks identifies critical microbes.
- WSGMB accurately identifies microbial biomarkers, outperforming existing methods in validation studies.
Conclusions:
- WSGMB provides a robust computational framework for microbial biomarker discovery.
- The method enhances the understanding of microbe roles in health and disease.
- Accurate biomarker identification using WSGMB supports precise clinical applications.
