Learning multiple evolutionary pathways from cross-sectional data
Learning multiple evolutionary pathways from cross-sectional data
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DOI：
10.1089/cmb.2005.12.584
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发表时间：
2005-07-01
影响因子：
1.7
通讯作者：
Lengauer, T
中科院分区：
文献类型：
--
作者：
Beerenwinkel, N;Rahnenführer, J;Lengauer, T
我们引入了树木的混合模型来描述以永久遗传变化的有序积累为特征的进化过程。该模型的基本构建块是有向加权树，它生成遗传事件所有模式集的概率分布。我们提出了一种类似于 EM 的算法来学习 K 树的混合模型，并展示了如何使用最大似然方法确定 K。作为一个案例研究，我们考虑了与耐药性相关的 HIV-1 逆转录酶突变的积累。拟合模型作为密度估计器进行了统计验证，并分析了模型拓扑的稳定性。我们获得了与生物学知识一致的艾滋病毒耐药性发展的生成概率模型。讨论了该模型的进一步应用和扩展。
We introduce a mixture model of trees to describe evolutionary processes that are characterized by the ordered accumulation of permanent genetic changes. The basic building block of the model is a directed weighted tree that generates a probability distribution on the set of all patterns of genetic events. We present an EM-like algorithm for learning a mixture model of K trees and show how to determine K with a maximum likelihood approach. As a case study, we consider the accumulation of mutations in the HIV-1 reverse transcriptase that are associated with drug resistance. The fitted model is statistically validated as a density estimator, and the stability of the model topology is analyzed. We obtain a generative probabilistic model for the development of drug resistance in HIV that agrees with biological knowledge. Further applications and extensions of the model are discussed.