Article
AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.
Nature communications - 6 Mar 2026
Nguyen Hoai-An, Peleg Anton Y, Wisniewski Jessica A, Wang Xiaoyu, Wang Zhikang, Blakeway Luke V, Badoordeen Gnei Z, Theegala Ravali, Doan Nhu Quynh, Parker Matthew H, Green Anna G, Song Jiangning, Dowe David L, Macesic Nenad
Abstract excerpt
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 phenotype prediction. To fully explore these high-resolution data, we propose AMR-GNN, a graph deep learning-based framework that integrates multiple genomic representations...
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