A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships
Abstract Background Predicting associations between microbes and diseases is crucial for clinical diagnosis and therapy. However, biological experiments are time-intensive, necessitating efficient computational models. Traditional models rely on existing microbe-disease associations, but limited dat...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-03093-6 |
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| author | Shaopeng Liu Wanlu Hu Chun-Chun Wang Linlin Zhuo Xu Lu |
| author_facet | Shaopeng Liu Wanlu Hu Chun-Chun Wang Linlin Zhuo Xu Lu |
| author_sort | Shaopeng Liu |
| collection | DOAJ |
| description | Abstract Background Predicting associations between microbes and diseases is crucial for clinical diagnosis and therapy. However, biological experiments are time-intensive, necessitating efficient computational models. Traditional models rely on existing microbe-disease associations, but limited data often restricts their effectiveness. This scarcity of information hinders the construction of a comprehensive association network, prompting the need for innovative solutions. Methods We propose RKGATMDA, a deep learning framework for microbe-disease association prediction. Utilizing a graph attention network, RKGATMDA learns representations from the microbe-disease association network. To address the limitation of insufficient association information, we introduce Random K-Nearest Neighbors to uncover latent relationships, enhancing representation learning. During each training iteration, associations are expanded based on attention scores, and a multi-head attention mechanism integrates diverse features, enabling RKGATMDA to capture comprehensive interactions between microbes and diseases. Results Results Experimental results show that RKGATMDA achieves AUC values of 0.8906 in 5-fold cross-validation, 0.8999 in global leave-one-out cross-validation, and 0.7246 in local leave-one-out cross-validation, outperforming previous methods such as ABHMDA, KATZHMDA, LRLSHMDA, BiRWHMDA, and NTSHMDA. Case studies on asthma, colon cancer, and colorectal carcinoma further validate its predictive power. Conclusion Our findings demonstrate that RKGATMDA effectively predicts microbe-disease associations, with at least 9 out of the top 10 prediction pairs validated by biological evidence. This highlights the potential of RKGATMDA as a valuable tool in microbial-disease research, offering a promising approach for identifying novel associations and advancing our understanding of microbial pathogenesis. |
| format | Article |
| id | doaj-art-eef85487b2164a668a6ce9c693061d5e |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-eef85487b2164a668a6ce9c693061d5e2025-08-20T03:46:00ZengBMCBMC Medical Informatics and Decision Making1472-69472025-07-0125111410.1186/s12911-025-03093-6A new approach for microbe-disease association prediction: incorporating representation learning of latent relationshipsShaopeng Liu0Wanlu Hu1Chun-Chun Wang2Linlin Zhuo3Xu Lu4School of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Computer Science, Guangdong Polytechnic Normal UniversitySchool of Science, Jiangnan UniversitySchool of Data Science and Artificial Intelligence, Wenzhou University of TechnologySchool of Computer Science, Guangdong Polytechnic Normal UniversityAbstract Background Predicting associations between microbes and diseases is crucial for clinical diagnosis and therapy. However, biological experiments are time-intensive, necessitating efficient computational models. Traditional models rely on existing microbe-disease associations, but limited data often restricts their effectiveness. This scarcity of information hinders the construction of a comprehensive association network, prompting the need for innovative solutions. Methods We propose RKGATMDA, a deep learning framework for microbe-disease association prediction. Utilizing a graph attention network, RKGATMDA learns representations from the microbe-disease association network. To address the limitation of insufficient association information, we introduce Random K-Nearest Neighbors to uncover latent relationships, enhancing representation learning. During each training iteration, associations are expanded based on attention scores, and a multi-head attention mechanism integrates diverse features, enabling RKGATMDA to capture comprehensive interactions between microbes and diseases. Results Results Experimental results show that RKGATMDA achieves AUC values of 0.8906 in 5-fold cross-validation, 0.8999 in global leave-one-out cross-validation, and 0.7246 in local leave-one-out cross-validation, outperforming previous methods such as ABHMDA, KATZHMDA, LRLSHMDA, BiRWHMDA, and NTSHMDA. Case studies on asthma, colon cancer, and colorectal carcinoma further validate its predictive power. Conclusion Our findings demonstrate that RKGATMDA effectively predicts microbe-disease associations, with at least 9 out of the top 10 prediction pairs validated by biological evidence. This highlights the potential of RKGATMDA as a valuable tool in microbial-disease research, offering a promising approach for identifying novel associations and advancing our understanding of microbial pathogenesis.https://doi.org/10.1186/s12911-025-03093-6MicrobeDiseaseAssociation predictionGraph attention networks |
| spellingShingle | Shaopeng Liu Wanlu Hu Chun-Chun Wang Linlin Zhuo Xu Lu A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships BMC Medical Informatics and Decision Making Microbe Disease Association prediction Graph attention networks |
| title | A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships |
| title_full | A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships |
| title_fullStr | A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships |
| title_full_unstemmed | A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships |
| title_short | A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships |
| title_sort | new approach for microbe disease association prediction incorporating representation learning of latent relationships |
| topic | Microbe Disease Association prediction Graph attention networks |
| url | https://doi.org/10.1186/s12911-025-03093-6 |
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