SEMtree:基于树的结构学习方法与结构方程模型
Mario Grassi1, Barbara Tarantino1
1Department of Brain and Behavioral Sciences, University of Pavia, Pavia 27100, Italy.
Bioinformatics (Oxford, England)
|June 9, 2023
概括
通过分析表达数据,SEMtree识别了蛋白质与蛋白质相互作用网络中的功能模块. 这个R包有助于发现生物相关的子网络,用于疾病状态分析.
科学领域:
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
- 网络分析 网络分析
背景情况:
- 表达和蛋白质-蛋白质相互作用 (PPI) 数据的指数增长需要识别功能模块的方法.
- 识别特定条件的子网络对于理解细胞和疾病状态至关重要.
研究的目的:
- 提出SEMtree (),一套新的基于树的算法,用于在PPI网络中发现功能模块.
- 为分析具有高可靠性得分的网络区域提供一个用户友好的R包.
主要方法:
- SEMtree () 在结构方程建模框架中集成图形理论和可统计解释的参数.
- 它采用了对差异表达和基因-基因共同表达的统计测试.
- 通过基于Chu-Liu-Edmonds算法的因果添加树来检测和转换活跃子网络为定向树.
主要成果:
- SEMtree) 应用于COVID-19RNA-seq数据 (GSE172114) 和模拟数据集.
- 该方法有效地捕捉了生物相关的子网络,并清楚地可视化了指导路径.
- 与现有方法相比,SEMtree () 显示出良好的扰动提取和分类器性能.
结论:
- 在PPI网络中,SEMtree提供了一种有效的技术来识别PPI网络中的功能模块.
- R包SEMgraph为研究人员提供了一个用户友好的工具.
- 这种方法有助于揭示与细胞或疾病状态相关的过程特定信息.
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