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Abstract <p>Extensive research has underscored the intricate relationships between microbial communities and human diseases. Delving into these associations enhances our understanding of disease mechanisms and facilitates the development of novel therapeutic strategies. Although traditional biological methods for identifying microbe-disease association (MDA) are reliable, they often entail high costs, extended timelines, and substantial manual effort. To address these limitations, this study introduces GRNCFMDA, an advanced deep learning framework designed to improve MDA prediction efficiency. Initially, the model integrates functional and Gaussian interaction profile (GIP) similarities of microbes, along with semantic and GIP similarities of diseases, to construct a comprehensive heterogeneous network. A graph random neural network (GRAND) enhanced with attention mechanisms is then applied to derive informative high-order representations of microbe and disease nodes. This is followed by a neural collaborative filtering module that merges the strengths of generalized matrix factorization for linear modeling with the deep learning capacity of multilayer perceptrons for capturing nonlinear patterns. Performance evaluations based on five-fold cross-validation across HMDAD and Disbiome datasets show that GRNCFMDA consistently outperforms four existing MDA prediction models. Additionally, empirical case studies affirm the model’s practical utility in uncovering novel MDA. The implementation and datasets are publicly available at <a href="https://github.com/chenyunmolu/GRNCFMDA">https://github.com/chenyunmolu/GRNCFMDA</a>.</p> Graphical Abstract <p>Step 1: similarity calculation and construction of heterogeneous graphs; Step 2: GRAND with integrated attention mechanism for high order feature extraction; Step 3: NCF framework integrating GMF and MLP for MDA prediction. </p>

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Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Bo Wang,
  • Wenlong Zhao,
  • Xiaoxin Du,
  • Jianfei Zhang,
  • Jingyou Li,
  • Hang Sun

摘要

Abstract

Extensive research has underscored the intricate relationships between microbial communities and human diseases. Delving into these associations enhances our understanding of disease mechanisms and facilitates the development of novel therapeutic strategies. Although traditional biological methods for identifying microbe-disease association (MDA) are reliable, they often entail high costs, extended timelines, and substantial manual effort. To address these limitations, this study introduces GRNCFMDA, an advanced deep learning framework designed to improve MDA prediction efficiency. Initially, the model integrates functional and Gaussian interaction profile (GIP) similarities of microbes, along with semantic and GIP similarities of diseases, to construct a comprehensive heterogeneous network. A graph random neural network (GRAND) enhanced with attention mechanisms is then applied to derive informative high-order representations of microbe and disease nodes. This is followed by a neural collaborative filtering module that merges the strengths of generalized matrix factorization for linear modeling with the deep learning capacity of multilayer perceptrons for capturing nonlinear patterns. Performance evaluations based on five-fold cross-validation across HMDAD and Disbiome datasets show that GRNCFMDA consistently outperforms four existing MDA prediction models. Additionally, empirical case studies affirm the model’s practical utility in uncovering novel MDA. The implementation and datasets are publicly available at https://github.com/chenyunmolu/GRNCFMDA.

Graphical Abstract

Step 1: similarity calculation and construction of heterogeneous graphs; Step 2: GRAND with integrated attention mechanism for high order feature extraction; Step 3: NCF framework integrating GMF and MLP for MDA prediction.