A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joiningReportar como inadecuado




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BMC Bioinformatics

, 9:S4

First Online: 28 May 2008

Abstract

BackgroundInference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange NNI, Subtree Prune and Regraft SPR and Tree Bisection and Reconnection TBR. However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement p-ECR. However, due to its high computation complexity, p-ECR has rarely been used in practice.

ResultsTo make the p-ECR move more efficient, this paper proposes a new method named p-ECRNJ. The main idea of p-ECRNJ is to use neighbor joining NJ to refine the unresolved nodes produced in p-ECR.

ConclusionExperiments with real datasets show that p-ECRNJ can find better trees than the best known maximum likelihood methods so far and can efficiently improve local topological transforms in reasonable time.

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Autor: Mao-Zu Guo - Jian-Fu Li - Yang Liu

Fuente: https://link.springer.com/







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