A graph neural network-based method for predicting interactions between plants

CUI Ziwen; WANG Huan; LI Han; WEI Le

doi:10.13300/j.cnki.hnlkxb.2025.02.031

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A graph neural network-based method for predicting interactions between plants

Techniques and methods | 更新时间：2026-11-05

- A graph neural network-based method for predicting interactions between plants
- Journal of Huazhong Agricultural University Vol. 44, Issue 2, Pages: 301-310(2025)
- 作者机构：
  
  华中农业大学信息学院，武汉430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.02.031
  CLC： TP18
- Received：19 August 2024，
  
  Published：06 March 2025
- 稿件说明：
移动端阅览
崔子文，王欢，李函，等.基于图神经网络的植物间相互作用预测方法［J］.华中农业大学学报，2025，44（2）：301‐310.

CUI Ziwen,WANG Huan,LI Han,et al.A graph neural network-based method for predicting interactions between plants[J].Journal of Huazhong Agricultural University,2025,44(02):301-310.
崔子文，王欢，李函，等.基于图神经网络的植物间相互作用预测方法［J］.华中农业大学学报，2025，44（2）：301‐310. DOI： 10.13300/j.cnki.hnlkxb.2025.02.031.

CUI Ziwen,WANG Huan,LI Han,et al.A graph neural network-based method for predicting interactions between plants[J].Journal of Huazhong Agricultural University,2025,44(02):301-310. DOI： 10.13300/j.cnki.hnlkxb.2025.02.031.

摘要

针对如何精准地根据先验知识预测植物间的抑制与促进相互作用问题，构建基于植物间相互作用的异质网络，提出一种基于图神经网络的相互作用预测方法。该方法主要由表征学习器、作用识别器和类型分类器三大模块构成。表征学习器负责提取植物间相互作用特征，作用识别器与表征学习器共同预测植物节点之间是否产生相互作用，并对类型分类器施加干扰，以最大程度地获得在不同类型相互作用间可迁移的特征。类型分类器旨在准确区分植物间相互作用的类型，以避免受到作用识别器的干扰。最后，基于三大模块之间的相互对抗关系来增强对植物间抑制与促进相互作用类型差异的鲁棒性，从而有效应对目标植物间相互作用的预测问题。该方法在伴生植物数据集上的AUC、精确率（precision）和准确率（accuracy）的表现相较于现有的SEAL、GATNE、HeGAN、PME、SVM和RF方法中的最优方法SVM分别提高了7.74、1.61和8.62百分点，分别达到了92.00%、80.12%和86.21%。结果表明，该方法通过降低目标类型差异的干扰，可以精准地预测植物间的相互作用，可以应用于优化农业生产实践。

Abstract

A heterogeneous network-based on interactions between plants was constructed，and a graph neural network-based method for predicting interactions between plants was proposed to solve the problem of accurately predicting inhibitory and promotive interactions between plants based on prior knowledge.The method is primarily composed of three modules including a representation learner，an interaction identifier，and a type classifier.The representation learner is responsible for extracting representations of interactions.The interaction identifier collaborates with the representation learner to predict whether an interaction exists on types of targeted plant and applies perturbations to the type classifier to maximally acquire transferable features across different types of interaction.The type classifier is designed to accurately distinguish types of interactions between plants to avoid being affected by the interaction identifier.The inter adversarial relationship among the three modules is leveraged to enhance robustness against the differences in the types of inhibitory and promotive interactions between plants，thereby effectively solving the problem of accurately predicting targeted interactions between plants.The AUC，precision，and accuracy of this method on the dataset of companion plant improved by 7.74，1.61，and 8.62 per cent，respectively compared with the best method SVM that of existing methods including SEAL，GATNE，HeGAN，PME，SVM，and RF，reaching 92.00%，80.12%，and 86.21%，respectively.The results indicate that the proposed method effectively mitigates the interference caused by target interaction type differences，enabling accurate prediction of plant interactions.This approach can be applied to optimize agricultural production practices.

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references

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