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AMR-GNN: A multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction

2025-07-27

Abstract excerpt

<h4>ABSTRACT</h4> Whole-genome sequencing (WGS) data are an invaluable resource for understanding antimicrobial resistance (AMR) mechanisms. However, WGS data are high-dimensional and the lack of standardized genomic representations is a key barrier to AMR prediction. To fully explore these high-resolution data, we propose AMR-GNN, a graph deep learning-based framework that integrates multiple genomic representat...

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Literature Corpus work
8eced1d1-9a82-5608-ad2f-488d4db5fae4
DOI
10.1101/2025.07.24.666581
Open publication

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AMR-GNN: A multi-representation graph neural network framework to enable genomic antimicrobial resistance predictionDOI 10.1101/2025.07.24.666581
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