MADGAN:A microbe-disease association prediction model based on generative adversarial networks
Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1159076/full |
| _version_ | 1827981735347879936 |
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| author | Weixin Hu Xiaoyu Yang Lei Wang Lei Wang Xianyou Zhu |
| author_facet | Weixin Hu Xiaoyu Yang Lei Wang Lei Wang Xianyou Zhu |
| author_sort | Weixin Hu |
| collection | DOAJ |
| description | Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks. To our knowledge, it is the first attempt to use the generative adversarial network to complete this important task. In MADGAN, we firstly constructed different features for microbes and diseases based on multiple similarity metrics. And then, we further adopted graph convolution neural network (GCN) to derive different features for microbes and diseases automatically. Finally, we trained MADGAN to identify latent microbe-disease associations by games between the generation network and the decision network. Especially, in order to prevent over-smoothing during the model training process, we introduced the cross-level weight distribution structure to enhance the depth of the network based on the idea of residual network. Moreover, in order to validate the performance of MADGAN, we conducted comprehensive experiments and case studies based on databases of HMDAD and Disbiome respectively, and experimental results demonstrated that MADGAN not only achieved satisfactory prediction performances, but also outperformed existing state-of-the-art prediction models. |
| first_indexed | 2024-04-09T22:13:45Z |
| format | Article |
| id | doaj.art-48cf255e01e14498b4f3308d7070911a |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-09T22:13:45Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-48cf255e01e14498b4f3308d7070911a2023-03-23T05:53:17ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-03-011410.3389/fmicb.2023.11590761159076MADGAN:A microbe-disease association prediction model based on generative adversarial networksWeixin Hu0Xiaoyu Yang1Lei Wang2Lei Wang3Xianyou Zhu4College of Computer Science and Technology, Hengyang Normal University, Hengyang, ChinaInstitute of Bioinformatics Complex Network Big Data, Changsha University, Changsha, ChinaInstitute of Bioinformatics Complex Network Big Data, Changsha University, Changsha, ChinaBig Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, ChinaCollege of Computer Science and Technology, Hengyang Normal University, Hengyang, ChinaResearches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks. To our knowledge, it is the first attempt to use the generative adversarial network to complete this important task. In MADGAN, we firstly constructed different features for microbes and diseases based on multiple similarity metrics. And then, we further adopted graph convolution neural network (GCN) to derive different features for microbes and diseases automatically. Finally, we trained MADGAN to identify latent microbe-disease associations by games between the generation network and the decision network. Especially, in order to prevent over-smoothing during the model training process, we introduced the cross-level weight distribution structure to enhance the depth of the network based on the idea of residual network. Moreover, in order to validate the performance of MADGAN, we conducted comprehensive experiments and case studies based on databases of HMDAD and Disbiome respectively, and experimental results demonstrated that MADGAN not only achieved satisfactory prediction performances, but also outperformed existing state-of-the-art prediction models.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1159076/fullmicrobe-disease associationsgraph convolution neural networkgenerative adversarial networkresidual networkcomputational prediction model |
| spellingShingle | Weixin Hu Xiaoyu Yang Lei Wang Lei Wang Xianyou Zhu MADGAN:A microbe-disease association prediction model based on generative adversarial networks Frontiers in Microbiology microbe-disease associations graph convolution neural network generative adversarial network residual network computational prediction model |
| title | MADGAN:A microbe-disease association prediction model based on generative adversarial networks |
| title_full | MADGAN:A microbe-disease association prediction model based on generative adversarial networks |
| title_fullStr | MADGAN:A microbe-disease association prediction model based on generative adversarial networks |
| title_full_unstemmed | MADGAN:A microbe-disease association prediction model based on generative adversarial networks |
| title_short | MADGAN:A microbe-disease association prediction model based on generative adversarial networks |
| title_sort | madgan a microbe disease association prediction model based on generative adversarial networks |
| topic | microbe-disease associations graph convolution neural network generative adversarial network residual network computational prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1159076/full |
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