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PubMed · 16108705

Learning multiple evolutionary pathways from cross-sectional data.

Niko Beerenwinkel·Jörg Rahnenführer·Martin Däumer·Daniel Hoffmann·Rolf Kaiser·Joachim Selbig·Thomas Lengauer
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Abstract

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.

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AlgorithmsAnti-HIV AgentsBiological EvolutionComputer SimulationCross-Sectional StudiesDrug Resistance, ViralGenetic VariationHIV Reverse TranscriptaseHIV-1HumansLearningLikelihood FunctionsModels, GeneticMutagenesisMutationPattern Recognition, AutomatedSignal Processing, Computer-AssistedJournal ArticleResearch Support, Non-U.S. Gov't

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Niko Beerenwinkel, Jörg Rahnenführer, Martin Däumer, Daniel Hoffmann, Rolf Kaiser, Joachim Selbig, Thomas Lengauer. Learning multiple evolutionary pathways from cross-sectional data.. https://doi.org/10.1089/cmb.2005.12.584

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