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基于莱维飞行和高斯分布的基因表达数据的增强型三重集群 $ \delta $-Trimax 方法与模糊的可可搜索
Titin Siswantining1, Muhamad Ido Raskapati1, Nisa Nurul Hidayah1
1Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia.
Mathematical biosciences and engineering : MBE
|January 29, 2026
概括
这项研究引入了一种混合的三集成方法,将delta-trimax和模糊的子搜索结合起来,用于分析基因表达数据. 该方法有效地识别了纤维细胞和心脏病数据集中的生物相关基因模块,为疾病机制提供了洞察力.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 基因表达数据分析对于理解复杂的生物过程至关重要.
- 传统的聚类方法可能无法完全捕捉高维基因表达数据集中的复杂关系.
- 三重聚合提供了一种强大的方法,可以同时识别跨基因,条件和时间点的模式.
研究的目的:
- 开发和评估一个新的混合三聚类框架,集成delta-trimax和模糊的古柯搜索 (fcs).
- 应用这个框架来分析来自原发性纤维细胞和心脏病样本的基因表达数据.
- 发现具有生物意义的基因模块,并深入了解疾病机制.
主要方法:
- 这项研究采用了混合三聚类方法,将$\delta$-Trimax算法与模糊子搜索 (FCS) 结合起来.
- 使用Lévy飞行和高斯分布的FCS,从$\delta$-Trimax优化了初始三元集群,以进行增强的全球和本地搜索.
- 三集群质量指数 (TQI) 用于评估跨基因,条件和时间点识别的三集群的连贯性.
主要成果:
- 混合方法应用于纤维细胞和心脏病基因表达数据集,分别确定了0.015和0.026的最佳delta值.
- 分析显示,心脏病数据集中的多个时间点具有显著的基因覆盖,具有显著的三重集.
- 使用基因本体学 (GO) 和基因和基因组的京都百科全书 (KEGG) 途径的功能丰富分析突出了NF-$\kappa$B,TGF-$\beta$和信号传递等重要途径.
结论:
- 拟议的混合三化框架有效地从复杂的基因表达数据中识别出具有生物意义的基因模块.
- 已识别的途径在机制上与免疫调节,细胞外矩阵组织和心肌功能有关.
- 这种方法对纤维化和心血管疾病背后的分子机制提供了宝贵的见解.
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