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Updated: May 17, 2025

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GMAMDA: Predicting Metabolite-Disease Associations Based on Adaptive Hardness Negative Sampling and Adaptive Graph

Binglu Hu1, Ying Su2,3, Xuecong Tian2

  • 1College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China.

Journal of Chemical Information and Modeling
|May 15, 2025
PubMed
Summary

This study introduces GMAMDA, a novel model for predicting metabolite-disease associations by integrating graph convolutions and adaptive negative sampling. GMAMDA improves accuracy in identifying disease-related metabolites for better diagnostics and treatment.

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Area of Science:

  • Biochemistry and Bioinformatics
  • Computational Biology
  • Medical Informatics

Background:

  • Metabolite concentrations are linked to disease development and progression.
  • Accurate prediction of metabolite-disease associations is vital for early diagnosis and treatment.
  • Existing prediction models often fail to integrate node features and consider graph structure effectively.

Purpose of the Study:

  • To develop a novel model, GMAMDA, for enhanced metabolite-disease association prediction.
  • To address limitations in existing methods regarding feature integration and negative sampling.
  • To improve the stability and accuracy of predicting potential metabolite-disease links.

Main Methods:

  • Constructed multiple heterogeneous graphs using multisource similarity information for metabolites and diseases.
  • Employed adaptive graph multiconvolution to generate rich node representations from varied hop neighborhoods.
  • Utilized adaptive hardness negative sampling with PCA for selecting high-information negative samples during training.

Main Results:

  • GMAMDA demonstrated superior performance over state-of-the-art methods.
  • Achieved high evaluation metrics: AUC (0.9962), AUPR (0.9967), and accuracy (0.9733).
  • Case studies on Alzheimer's and kidney diseases confirmed GMAMDA's clinical utility in identifying metabolite markers.

Conclusions:

  • GMAMDA offers a robust and accurate approach for metabolite-disease association prediction.
  • The model's novel strategies enhance prediction stability and clinical relevance.
  • GMAMDA holds significant potential for advancing precision medicine through biomarker discovery.