用图神经网络预测微生物间正负作用，准确率超80%。
Predicting Microbial Interactions Using Graph Neural Networks
- 构建微生物互作图谱，用GNN捕捉跨实验共享信息。
- 在7500+互作数据上实现80.44%的F1分数，优于传统方法。
- 可识别互惠、竞争等复杂关系，适合生态建模与合成菌群设计。
预测物种间相互作用是微生物生态学中的关键挑战，因为这些作用决定了微生物群落的结构与功能。本文利用单培养生长能力、与其他物种的互作数据及系统发育信息，预测微生物间作用方向（正或负）。基于迄今最大的成对互作数据集，包含20个物种在40种碳源条件下的7500余次互作，重点参考Nestor等人的工作[28]。我们提出使用图神经网络（GNN）作为强分类器，构建微生物互作边图以挖掘跨共培养实验的共享信息，并用于预测作用模式。模型不仅能判别正/负互作，还可分类互惠、竞争、寄生等复杂类型。初步结果令人鼓舞，F1得分达80.44%，显著高于文献中类似方法（如XGBoost的72.76%）。
原文摘要 · Abstract (English)
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%.
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