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A graph neural network-driven method for predicting plant-plant interactions
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    Abstract:

    In response to the challenge of accurately predicting plant-plant inhibitory and promotive interactions based on prior knowledge, this study constructs a heterogeneous network derived from plant interactions and proposes a graph neural network-driven method to tackle this issue. The method is primarily composed of three modules: 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 target plant types and applies perturbations to the type classifier to maximally acquire transferable features across different interaction types. The type classifier is designed to accurately distinguish between types of plant interactions to avoid being influenced by the interaction identifier. Finally, the inter adversarial relationship among the three modules is leveraged to enhance robustness against the differences in the types of inhibitory and promotive interactions, thereby effectively addressing the prediction challenge of plant-plant interactions. This method demonstrated improvements in AUC, Precision, and Accuracy by 7.74%, 1.61%, and 8.62%, respectively, compared to existing methods, achieving scores of 92.00%, 80.12%, and 86.21% on a companion plant dataset. The results indicate that the method accurately predicts interactions between plants by effectively reducing the interference from target types.

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History
  • Received:October 03,2023
  • Revised:August 19,2024
  • Adopted:February 21,2025
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