一个新的基于相似性的适应卢温算法 (SIMBA) 用于在p值赋值的生物网络中识别主动模块
Nina Singlan1, Fadi Abou Choucha2, Claude Pasquier2
1Université Côte d'Azur, CNRS, i3S, 06560, Valbonne, France. nsinglan@i3s.unice.fr.
Scientific reports
|April 2, 2025
概括
本研究引入了一种新方法,通过将网络结构与节点属性集成来分析复杂的生物网络. 该方法增强了社区检测,以识别功能相关的生物模块.
科学领域:
- 计算生物学 计算生物学
- 网络科学 网络科学
- 生物信息学是一种生物信息学.
背景情况:
- 现实世界的网络,特别是生物网络,具有复杂的结构和节点属性,对分析构成挑战.
- 现有的社区检测算法经常优先考虑拓数据,忽略了用于识别功能子网络的关键属性基础相似性.
研究的目的:
- 开发一种新的分数方法,用于图形分区,该方法包含基于属性的相似性.
- 调整路凡算法以优化这个新的评分函数,以检测功能连贯的社区.
主要方法:
- 为节点属性开发了一个新的相似性函数.
- 路凡算法被修改,以优化对图形分区的拟议得分函数.
- 该方法在人工和现实世界生物网络数据集上进行了评估.
主要成果:
- 提出的方法成功地确定了密切联系且功能连贯的社区.
- 实验证明了新方法在现有的最先进方法上的优越性.
- 拓和基于属性的信息的整合被证明是有效的.
结论:
- 这种新的方法提供了一个强大的工具,用于在复杂网络中发现具有生物意义的模块.
- 这种方法通过考虑网络结构和节点属性,为复杂的生物过程提供了更深入的见解.
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