Graph neural networks reveal microbial association patterns in inflammatory bowel disease

[bioRxiv] A preprint using longitudinal metagenomic data from 106 individuals in HMP2/iHMP inferred individual-specific microbial association networks and used them to train and interpret graph neural network (GNN) models, comparing them with abundance-based models such as Random Forest (RF). According to the authors, RF achieved higher predictive performance (AUPRC ~0.59 vs 0.446), but interpreting the GNNs showed that their predictions relied on a densely connected module of co-abundant Bacteroidales species; unlike the taxa prioritized by RF, these species were not differentially abundant between conditions. The authors said that in an independent cohort this module carried most of the predictive signal, and removing its associations reduced performance to near chance. They concluded that coordinated association structure, rather than abundance shifts alone, is a feature of the IBD gut microbiome, and that interpreting GNNs can uncover such patterns to complement abundance-based analyses.