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计算机系统应用英文版:2024,33(10):87-96
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基于图自编码器与LightGBM的癌症驱动基因识别系统
(西南科技大学 计算机科学与技术学院, 绵阳 621010)
Identification System of Cancer Driver Genes Based on Graph Autoencoder and LightGBM
(School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China)
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Received:March 06, 2024    Revised:May 06, 2024
中文摘要: 在癌症的形成和进展中, 癌症驱动基因扮演着重要角色. 准确识别癌症驱动基因有助于深入理解癌症的发生机制, 推动精准医学的发展. 针对当前癌症驱动基因识别领域所面临的异质性和复杂性问题, 本文设计并实现了一种基于图自编码器与LightGBM的癌症驱动基因识别系统ACGAI. 该系统首先以无监督的方式通过图自编码器学习生物分子网络的复杂拓扑结构, 随后将生成的嵌入表示与原始基因特征进行拼接, 形成基因增强特征并输入至LightGBM. 在经过训练后, 系统输出生物分子网络上每个基因的预测得分, 实现了对癌症驱动基因的准确识别. 最终, 该系统利用Web技术创建了一套用户友好、交互性强的可视化界面, 实现在基因集分析场景中的癌症驱动基因识别, 并为识别结果提供了生物学解释. 经过测试, 该系统表现出优于其他方法的识别性能, 能有效识别癌症驱动基因.
Abstract:Cancer driver genes play a crucial role in the formation and progression of cancer. Accurate identification of cancer driver genes contributes to a deeper understanding of the mechanisms underlying cancer development and advances precision medicine. To address the heterogeneity and complexity challenges in the current field of cancer driver gene identification, this study presents the design and implementation of a cancer driver gene identification system, ACGAI, based on graph autoencoder and LightGBM. The system initially employs unsupervised learning with a graph autoencoder to grasp the complex topological structure of the biomolecular network. Subsequently, the generated embedding representations are concatenated with original gene features, forming gene-enhanced features input into LightGBM. After training, the system outputs predictive scores for each gene on the biomolecular network, achieving accurate identification of cancer driver genes. Finally, the system utilizes Web technology to create a user-friendly and highly interactive visualization interface, enabling cancer driver gene identification in the context of gene set analysis and providing biological interpretation for the identification results. Through rigorous testing, the system exhibits superior identification performance compared to other methods, demonstrating its effectiveness in identifying cancer driver genes.
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基金项目:四川省教育信息化与大数据中心2022年度课题(DSJ2022214)
引用文本:
谢兵,苏波.基于图自编码器与LightGBM的癌症驱动基因识别系统.计算机系统应用,2024,33(10):87-96
XIE Bing,SU Bo.Identification System of Cancer Driver Genes Based on Graph Autoencoder and LightGBM.COMPUTER SYSTEMS APPLICATIONS,2024,33(10):87-96