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
Hauptverfasser:	Wang, Zhina, Chen, Yangyuan, Ma, Hongming, Gao, Hong, Zhu, Yangbin, Wang, Hongwu, Zhang, Nan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media S.A 06.01.2025
Schlagworte:	artificial intelligence (AI) lung cancer Pharmacology redevelopment of traditional medicines snoRNA therapeutic targets variational graph autoencoder (VGAE) lung cancer snoRNA therapeutic targets redevelopment of traditional medicines artificial intelligence (AI) variational graph autoencoder (VGAE)
ISSN:	1663-9812, 1663-9812
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Zusammenfassung:	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 .
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Xiong Li, East China Jiaotong University, China Reviewed by: Xiangzheng Fu, Hunan University, China Edited by: Junlin Xu, Hunan University, China
ISSN:	1663-9812 1663-9812
DOI:	10.3389/fphar.2024.1529128