Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines
Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1529128/full |
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| author | Zhina Wang Zhina Wang Yangyuan Chen Hongming Ma Hongming Ma Hong Gao Hong Gao Yangbin Zhu Hongwu Wang Nan Zhang Nan Zhang |
| author_facet | Zhina Wang Zhina Wang Yangyuan Chen Hongming Ma Hongming Ma Hong Gao Hong Gao Yangbin Zhu Hongwu Wang Nan Zhang Nan Zhang |
| author_sort | Zhina Wang |
| collection | DOAJ |
| description | Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model’s effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data. |
| format | Article |
| id | doaj-art-c553efc486264dfca34df3bbee44c6bc |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-c553efc486264dfca34df3bbee44c6bc2025-01-06T06:59:26ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-01-011510.3389/fphar.2024.15291281529128Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicinesZhina Wang0Zhina Wang1Yangyuan Chen2Hongming Ma3Hongming Ma4Hong Gao5Hong Gao6Yangbin Zhu7Hongwu Wang8Nan Zhang9Nan Zhang10Department of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, ChinaDepartment of Oncology, Emergency General Hospital, Beijing, ChinaSchool of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, ChinaDepartment of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, ChinaDepartment of Oncology, Emergency General Hospital, Beijing, ChinaDepartment of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, ChinaDepartment of Oncology, Emergency General Hospital, Beijing, ChinaSchool of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, ChinaRespiratory Disease Center, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, ChinaDepartment of Oncology, Emergency General Hospital, Beijing, ChinaExisting studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model’s effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data.https://www.frontiersin.org/articles/10.3389/fphar.2024.1529128/fullredevelopment of traditional medicinessnoRNA therapeutic targetslung cancervariational graph autoencoder (VGAE)artificial intelligence (AI) |
| spellingShingle | Zhina Wang Zhina Wang Yangyuan Chen Hongming Ma Hongming Ma Hong Gao Hong Gao Yangbin Zhu Hongwu Wang Nan Zhang Nan Zhang Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines Frontiers in Pharmacology redevelopment of traditional medicines snoRNA therapeutic targets lung cancer variational graph autoencoder (VGAE) artificial intelligence (AI) |
| title | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
| title_full | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
| title_fullStr | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
| title_full_unstemmed | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
| title_short | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
| title_sort | accurate identification of snorna targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
| topic | redevelopment of traditional medicines snoRNA therapeutic targets lung cancer variational graph autoencoder (VGAE) artificial intelligence (AI) |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1529128/full |
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