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权重的中间体树:在单标记的瘤突变树推断推断中总结基因的一个一般方法
Hamed Vasei1, Mohammad-Hadi Foroughmand-Araabi1, Amir Daneshgar1
1Department of Mathematical Sciences, Sharif University of Technology, Tehran 111559415, Iran.
Bioinformatics (Oxford, England)
|July 10, 2024
概括
我们介绍了一种新方法,将癌症进化树总结为一个代表性树. 我们的方法,加权的中枢神经树问题,为重建癌症进化史提供了卓越的性能.
科学领域:
- 计算生物学 计算生物学
- 进化生物学 进化生物学
- 生物信息学是一种生物信息学.
背景情况:
- 瘤树对于通过描绘进化过程来理解癌症演变至关重要.
- 将多个瘤树总结为一个代表性树,有助于发现反复出现的进化模式.
- 产生共识树的现有方法包括GraPhyC和TuELiP.
研究的目的:
- 定义和解决加权的中位点树问题,以总结扎根树的集合.
- 建立一个包含现有的共识树算法和指标的框架.
- 为NP-hard祖先嵌入案例开发一个高效的算法.
主要方法:
- 将根植的树木映射到欧几里德空间.
- 计算映射树的加权中心矩阵.
- 识别离中心矩阵最近的映射树 (NMTP).
- 开发NMTP的整数线性程序和用于祖先嵌入的新算法 (2-AncL2).
主要成果:
- 权重的中间体树问题统一了各种各样的共识树指标和算法.
- 对于邻近性嵌入,NMTP是多项式时间可解决的,但对于祖先和距离映射,它是NP-hard的.
- 与现有的共识树方法相比,2-AncL2算法显示出更高的性能.
- 对乳腺癌数据集的应用表明,心状细胞树有效地总结了进化信息.
结论:
- 权重的中间体树框架为代表性树结构提供了一个强大的方法.
- 2-AncL2算法为重建癌症进化史提供了一个有效的解决方案.
- 从这种方法获得的代表性瘤树包含可靠的进化见解.
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