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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| Veröffentlicht in: | Frontiers in pharmacology Jg. 15; S. 1529128 |
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06.01.2025
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| Abstract | 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
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| AbstractList | 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. 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.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. 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 . |
| Author | Chen, Yangyuan Ma, Hongming Gao, Hong Wang, Hongwu Zhang, Nan Wang, Zhina Zhu, Yangbin |
| AuthorAffiliation | 1 Department of Pulmonary and Critical Care Medicine II , Emergency General Hospital , Beijing , China 3 School of Data Science and Artificial Intelligence , Wenzhou University of Technology , Wenzhou , China 4 Respiratory Disease Center , Dongzhimen Hospital , Beijing University of Chinese Medicine , Beijing , China 2 Department of Oncology , Emergency General Hospital , Beijing , China |
| AuthorAffiliation_xml | – name: 4 Respiratory Disease Center , Dongzhimen Hospital , Beijing University of Chinese Medicine , Beijing , China – name: 1 Department of Pulmonary and Critical Care Medicine II , Emergency General Hospital , Beijing , China – name: 2 Department of Oncology , Emergency General Hospital , Beijing , China – name: 3 School of Data Science and Artificial Intelligence , Wenzhou University of Technology , Wenzhou , China |
| Author_xml | – sequence: 1 givenname: Zhina surname: Wang fullname: Wang, Zhina – sequence: 2 givenname: Yangyuan surname: Chen fullname: Chen, Yangyuan – sequence: 3 givenname: Hongming surname: Ma fullname: Ma, Hongming – sequence: 4 givenname: Hong surname: Gao fullname: Gao, Hong – sequence: 5 givenname: Yangbin surname: Zhu fullname: Zhu, Yangbin – sequence: 6 givenname: Hongwu surname: Wang fullname: Wang, Hongwu – sequence: 7 givenname: Nan surname: Zhang fullname: Zhang, Nan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39834830$$D View this record in MEDLINE/PubMed |
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| Keywords | lung cancer snoRNA therapeutic targets redevelopment of traditional medicines artificial intelligence (AI) variational graph autoencoder (VGAE) |
| Language | English |
| License | Copyright © 2025 Wang, Chen, Ma, Gao, Zhu, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| References | Sun (B31) 2022; 23 Wang (B34); 276 Wei (B43); 157 Zhao (B50) 2022; 20 Esteller (B5) 2011; 12 Wang (B32); 25 Hou (B6) 2022; 18 Kipf (B8) 2016 Liu (B20) 2017 Zhang (B46); 17 Ranjan (B29) 2017 Zheng (B52) 2018 Zhang (B48) 2023; 10 Zhou (B56); 35 Ning (B26) 2021; 49 Ning (B27) 2023; 24 Zhang (B47); 35 Ma (B23); 40 Zhuo (B59) 2023; 24 Cho (B3) 2024 Liu (B22) 2024 Liu (B21); 24 Qi (B28) 2016; 6 Li (B13) 2020; 36 Liu (B19); 24 Krishnan (B12) 2016; 11 Kishore (B9) 2006; 311 Kiss-László (B10) 1996; 85 Zhou (B54) 2023; 163 Zheng (B51) 2015; 34 Chen (B2) 2023; 51 Liao (B17) 2010; 9 Wei (B44); 24 Zhang (B45); 33 Zhou (B57); 25 Mannoor (B25) 2012; 1826 Zhuo (B58) 2022; 23 Wang (B35); 25 Ahn (B1) 2021 Ma (B24); 174 Wei (B40) Ding (B4) 2021; 26 Wei (B41); 15 Zhou (B55); 40 Wei (B39); 171 Li (B15) 2023; 9 Liao (B18) 2023; 14 Zhang (B49) St (B30) 1989; 6 Zhou (B53); 23 Wang (B33); 64 Wang (B37) 2019; 9 Hu (B7) 2024; 25 Li (B16); 160 Wang (B36); 20 Wang (B38) Kobayashi (B11) 2020 Wei (B42); 22 Li (B14) |
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| SubjectTerms | artificial intelligence (AI) lung cancer Pharmacology redevelopment of traditional medicines snoRNA therapeutic targets variational graph autoencoder (VGAE) |
| Title | Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines |
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