通过网络嵌入和动态优化实现生物医学复合网络的自动化社区检测
Haonan Liu1, Wen Shi2, Xiaoyu Li3
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, PR China; Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, PR China; University of Chinese Academy of Sciences, Beijing 100190, PR China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, PR China.
Computer methods and programs in biomedicine
|October 28, 2025
概括
通过检测复杂网络中的患者社区,LesNet通过自动化生物医学数据集成和分析. 这种框架显著提高了准确性和效率,减少了精准医学中的手工劳动.
科学领域:
- 生物医学信息学 生物医学信息学
- 网络科学 网络科学
- 计算生物学 计算生物学
背景情况:
- 整合多来源的生物医学数据 (EHR,多omics) 对于了解疾病机制至关重要.
- 目前的方法与数据异质性,模糊的社区界限和手动干预作斗争.
- 这些局限性阻碍了对复杂的生物医学关系的全面分析.
研究的目的:
- 介绍LesNet,这是生物医学复合网络的第一个全自动化社区检测框架.
- 提高多源生物医学数据整合和分析的效率和准确性.
- 克服现有方法在处理异质生物医学数据方面的局限性.
主要方法:
- 莱斯网使用跨网络的动态对齐来解决数据异质性问题.
- 集成了拓结构和语义特征,使用自我监督的强化学习方法.
- 使用对比学习和强化学习来实现动态社区边界优化.
主要成果:
- 莱斯网在模拟的跨医院多omics数据上的准确性和效率显著提高.
- 与基线方法相比,在与疾病相关的模块检测方面,F1得分增加了15%.
- 自动化过程显示过效率比手动工作流程高出80%.
结论:
- 莱斯网有效地减少了手工工作,同时保持了生物医学数据分析的高性能.
- 提供了一个可扩展的,自动化工具,用于精准医学中的多源数据分析.
- 在癌症亚型分类和药物组推方面有潜在的应用.
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