Wang Zhina, Chen Yangyuan, Ma Hongming, Gao Hong, Zhu Yangbin, Wang Hongwu, Zhang Nan
Department of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, China.
Department of Oncology, Emergency General Hospital, Beijing, China.
Front Pharmacol. 2025 Jan 6;15:1529128. doi: 10.3389/fphar.2024.1529128. eCollection 2024.
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.
现有研究表明，小核仁RNA（snoRNA）的失调或异常表达与包括肺癌在内的多种疾病密切相关。此外，这些疾病往往涉及多个靶点，这使得传统药物的重新开发具有很大前景。准确预测潜在的snoRNA治疗靶点对于疾病的早期干预和传统药物的重新开发至关重要。此外，研究人员已经开发出基于人工智能（AI）的方法来筛选和预测潜在的snoRNA治疗靶点，从而推动传统药物的重新开发。然而，现有方法面临诸如数据集不平衡以及图神经网络（GNN）中高 degree 节点占主导地位等挑战，这会影响节点表示的准确性。为应对这些挑战，我们提出一种基于变分图自编码器（VGAE）的AI模型，该模型集成了解耦和柯尔莫哥洛夫 - 阿诺德网络（KAN）技术。该模型通过学习snoRNA和疾病表示来重建snoRNA - 疾病图，准确识别潜在的snoRNA治疗靶点。通过将相似性与节点度解耦，该模型减轻了高 degree 节点的主导地位，提高了在肺癌等场景中的预测准确性，并利用KAN技术提高了对新数据的适应性和灵活性。案例研究表明，snoRNA SNORA21和SNORD33在肺癌患者中异常表达，是潜在治疗靶点的有力候选者。这些发现验证了所提出模型在识别肺癌等疾病治疗靶点方面的有效性，支持早期筛查和治疗，并推动传统药物的重新开发。数据和实验结果存档于：https://github.com/shmildsj/data 。
