Learning multiple evolutionary pathways from cross-sectional data

@article{Beerenwinkel2004LearningME,
  title={Learning multiple evolutionary pathways from cross-sectional data},
  author={Niko Beerenwinkel and J{\"o}rg Rahnenf{\"u}hrer and Martin D{\"a}umer and Daniel Hoffmann and Rolf Kaiser and Joachim Selbig and Thomas Lengauer},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={2004},
  volume={12 6},
  pages={584-98}
}
We introduce a mixture model of trees to describe evolutionary processes that are characterized by the 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… CONTINUE READING

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