Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Prediction

• URL: http://arxiv.org/abs/2505.00236v1
• Date: Thu, 01 May 2025 01:06:05 GMT
• Title: Node2Vec-DGI-EL: A Hierarchical Graph Representation Learning Model for Ingredient-Disease Association Prediction
• Authors: Leifeng Zhang, Xin Dong, Shuaibing Jia, Jianhua Zhang,
• Abstract summary: This study proposes an ingredient-disease association prediction model (Node2Vec-DGI-EL) based on hierarchical graph representation learning.<n>To improve prediction accuracy and robustness, an ensemble learning method is incorporated to achieve more accurate ingredient-disease association predictions.
• Score: 8.749982610059874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional Chinese medicine, as an essential component of traditional medicine, contains active ingredients that serve as a crucial source for modern drug development, holding immense therapeutic potential and development value. A multi-layered and complex network is formed from Chinese medicine to diseases and used to predict the potential associations between Chinese medicine ingredients and diseases. This study proposes an ingredient-disease association prediction model (Node2Vec-DGI-EL) based on hierarchical graph representation learning. First, the model uses the Node2Vec algorithm to extract node embedding vectors from the network as the initial features of the nodes. Next, the network nodes are deeply represented and learned using the DGI algorithm to enhance the model's expressive power. To improve prediction accuracy and robustness, an ensemble learning method is incorporated to achieve more accurate ingredient-disease association predictions. The effectiveness of the model is then evaluated through a series of theoretical verifications. The results demonstrated that the proposed model significantly outperformed existing methods, achieving an AUC of 0.9987 and an AUPR of 0.9545, thereby indicating superior predictive capability. Ablation experiments further revealed the contribution and importance of each module. Additionally, case studies explored potential associations, such as triptonide with hypertensive retinopathy and methyl ursolate with colorectal cancer. Molecular docking experiments validated these findings, showing the triptonide-PGR interaction and the methyl ursolate-NFE2L2 interaction can bind stable. In conclusion, the Node2Vec-DGI-EL model focuses on TCM datasets and effectively predicts ingredient-disease associations, overcoming the reliance on node semantic information.

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