GCNPMDA : Human microbe-disease association prediction by hierarchical graph convolutional network with layer attention
(2025) In Biomedical Signal Processing and Control 100.- Abstract
Microorganisms play a crucial role in various physiological processes, including metabolism, immune defense, nutrition absorption, defense against cancer, and protection against pathogen colonization. Changes in microbial communities serve as potential biomarkers for diseases, offering significant insights into disease treatment and diagnosis. However, the association between microorganisms and diseases is still unclear, and more computational methods are needed to predict potential associations. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease... (More)
Microorganisms play a crucial role in various physiological processes, including metabolism, immune defense, nutrition absorption, defense against cancer, and protection against pathogen colonization. Changes in microbial communities serve as potential biomarkers for diseases, offering significant insights into disease treatment and diagnosis. However, the association between microorganisms and diseases is still unclear, and more computational methods are needed to predict potential associations. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease associations, microbe–microbe similarities, and disease–disease similarities into a heterogeneous network. The model utilizes a Graph Convolutional Network (GCN) to learn embeddings for diseases and microbes. To enhance attribute information, microbe–microbe similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and functional information, while disease–disease similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and symptom information. Additionally, attention mechanisms are applied to combine embeddings from multiple graph convolution layers. The model's predictive effectiveness is evaluated on Human Microbe-Disease Association Database (HMDAD). Leave-one-out cross-validation (LOOCV) was conducted. The Area Under ROC Curve (AUC) of LOOCV is 0.98. The 5-fold cross-validation (5-fold CV) on HMDAD yields average AUC of 0.98 ± 0.009. Furthermore, we carried out a case study of type 2 diabetes (T2D), inflammatory bowel disease (IBD), and rheumatoid arthritis. Based on existing literature evidence, it was confirmed that 6, 7, and 7 of the top-10 inferred microbes have established associations with T2D, IBD, and rheumatoid arthritis, respectively. GCNPMDA demonstrates potential efficacy in identifying disease-related microbes, offering a promising tool to uncover the intricate relationship between microorganisms and their human hosts.
(Less)
- author
- Wu, Chuanyan ; Lin, Bentao ; Zhang, Huanghe ; Xu, Da ; Gao, Rui ; Song, Rui ; Liu, Zhi Ping and De Marinis, Yang LU
- organization
- publishing date
- 2025-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Attention mechanism, Deep learning, Disease-microbe association prediction, Graph convolutional network
- in
- Biomedical Signal Processing and Control
- volume
- 100
- article number
- 107004
- publisher
- Elsevier
- external identifiers
-
- scopus:85205675177
- ISSN
- 1746-8094
- DOI
- 10.1016/j.bspc.2024.107004
- language
- English
- LU publication?
- yes
- id
- 5bc7dc8a-0698-4efc-9017-e56d1f142266
- date added to LUP
- 2024-11-26 15:30:48
- date last changed
- 2025-04-04 15:04:08
@article{5bc7dc8a-0698-4efc-9017-e56d1f142266,
abstract = {{<p>Microorganisms play a crucial role in various physiological processes, including metabolism, immune defense, nutrition absorption, defense against cancer, and protection against pathogen colonization. Changes in microbial communities serve as potential biomarkers for diseases, offering significant insights into disease treatment and diagnosis. However, the association between microorganisms and diseases is still unclear, and more computational methods are needed to predict potential associations. In this paper, we introduce a novel computational model, the Graph Convolutional Network to Predict Microbe-Disease Associations (GCNPMDA), which employs layer attention mechanisms (see Figure 1). GCNPMDA integrates known microbe-disease associations, microbe–microbe similarities, and disease–disease similarities into a heterogeneous network. The model utilizes a Graph Convolutional Network (GCN) to learn embeddings for diseases and microbes. To enhance attribute information, microbe–microbe similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and functional information, while disease–disease similarities are computed using Cosine similarity, Jaccard similarity, Gaussian kernel, and symptom information. Additionally, attention mechanisms are applied to combine embeddings from multiple graph convolution layers. The model's predictive effectiveness is evaluated on Human Microbe-Disease Association Database (HMDAD). Leave-one-out cross-validation (LOOCV) was conducted. The Area Under ROC Curve (AUC) of LOOCV is 0.98. The 5-fold cross-validation (5-fold CV) on HMDAD yields average AUC of 0.98 ± 0.009. Furthermore, we carried out a case study of type 2 diabetes (T2D), inflammatory bowel disease (IBD), and rheumatoid arthritis. Based on existing literature evidence, it was confirmed that 6, 7, and 7 of the top-10 inferred microbes have established associations with T2D, IBD, and rheumatoid arthritis, respectively. GCNPMDA demonstrates potential efficacy in identifying disease-related microbes, offering a promising tool to uncover the intricate relationship between microorganisms and their human hosts.</p>}},
author = {{Wu, Chuanyan and Lin, Bentao and Zhang, Huanghe and Xu, Da and Gao, Rui and Song, Rui and Liu, Zhi Ping and De Marinis, Yang}},
issn = {{1746-8094}},
keywords = {{Attention mechanism; Deep learning; Disease-microbe association prediction; Graph convolutional network}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Biomedical Signal Processing and Control}},
title = {{GCNPMDA : Human microbe-disease association prediction by hierarchical graph convolutional network with layer attention}},
url = {{http://dx.doi.org/10.1016/j.bspc.2024.107004}},
doi = {{10.1016/j.bspc.2024.107004}},
volume = {{100}},
year = {{2025}},
}