On the complexity of comparing evolutionary trees
Discrete Applied Mathematics - Special volume on computational molecular biology
Some Approximation Results for the Maximum Agreement Forest Problem
APPROX '01/RANDOM '01 Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation Techniques in Computer Science: Approximation, Randomization and Combinatorial Optimization
Computing the minimum number of hybridization events for a consistent evolutionary history
Discrete Applied Mathematics
A 3-approximation algorithm for the subtree distance between phylogenies
Journal of Discrete Algorithms
Hybridization in Nonbinary Trees
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Algorithms for Reticulate Networks of Multiple Phylogenetic Trees
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the Elusiveness of Clusters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MURPAR: a fast heuristic for inferring parsimonious phylogenetic networks from multiple gene trees
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
An algorithm for constructing parsimonious hybridization networks with multiple phylogenetic trees
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
A quadratic kernel for computing the hybridization number of multiple trees
Information Processing Letters
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Hybridization is a reticulate evolutionary process. An established problem on hybridization is computing the minimum number of hybridization events, called the hybridization number, needed in the evolutionary history of two phylogenetic trees. This problem is known to be NP-hard. In this paper, we present a new practical method to compute the exact hybridization number. Our approach is based on an integer linear programming formulation. Simulation results on biological and simulated datasets show that our method (as implemented in program SPRDist) is more efficient and robust than an existing method.