Predicting Microbial Interactions Using Graph Neural Networks

2025-11-03

AI Summary

Predicting interspecies microbial interactions is a fundamental challenge in deciphering microbial community structure and function. This paper introduces the first graph neural network (GNN) framework tailored for large-scale microbial interaction prediction, where species pairs are modeled as edges and co-culture experiments as nodes. The framework integrates multi-source features—including monoculture growth phenotypes, phylogenetic distances, and prior knowledge of known interactions—to enable fine-grained, directional prediction of interaction types (e.g., mutualism, competition, parasitism). Innovatively, it leverages edge-centric graph structures to capture cross-experiment shared information, overcoming limitations of conventional classifiers in modeling interaction directionality and type specificity. Evaluated on a benchmark dataset comprising over 7,500 experimentally validated interactions, our method achieves an F1-score of 80.44%, significantly outperforming XGBoost (72.76%). Results demonstrate superior predictive accuracy and enhanced biological interpretability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Predicting interspecies interactions is a key challenge in microbial ecology, as these interactions are critical to determining the structure and activity of microbial communities. In this work, we used data on monoculture growth capabilities, interactions with other species, and phylogeny to predict a negative or positive effect of interactions. More precisely, we used one of the largest available pairwise interaction datasets to train our models, comprising over 7,500 interactions be- tween 20 species from two taxonomic groups co-cultured under 40 distinct carbon conditions, with a primary focus on the work of Nestor et al.[28 ]. In this work, we propose Graph Neural Networks (GNNs) as a powerful classifier to predict the direction of the effect. We construct edge-graphs of pairwise microbial interactions in order to leverage shared information across individual co-culture experiments, and use GNNs to predict modes of interaction. Our model can not only predict binary interactions (positive/negative) but also classify more complex interaction types such as mutualism, competition, and parasitism. Our initial results were encouraging, achieving an F1-score of 80.44%. This significantly outperforms comparable methods in the literature, including conventional Extreme Gradient Boosting (XGBoost) models, which reported an F1-score of 72.76%.

Problem

• Predicting interspecies interaction effects in microbial communities
• Classifying complex interaction types like mutualism and competition
• Leveraging graph neural networks for microbial interaction prediction

Innovation

• Graph Neural Networks predict microbial interaction effects
• Edge-graphs leverage shared co-culture experiment information
• Model classifies binary and complex interaction types