New algorithms for the duplication-loss model
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
From Gene Trees to Species Trees
SIAM Journal on Computing
Bioinformatics
An Ω(n^2/ log n) Speed-Up of TBR Heuristics for the Gene-Duplication Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Gene-Duplication Problem: Near-Linear Time Algorithms for NNI-Based Local Searches
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Heuristics for the gene-duplication problem: a Θ(n) speed-up for the local search
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
A Note on the Fixed Parameter Tractability of the Gene-Duplication Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Fast local search for unrooted Robinson-foulds supertrees
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
From Gene Trees to Species Trees II: Species Tree Inference by Minimizing Deep Coalescence Events
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
GTP supertrees from unrooted gene trees: linear time algorithms for NNI based local searches
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
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Phylogenetic inference is a computationally difficult problem, and constructing high-quality phylogenies that can build upon existing phylogenetic knowledge and synthesize insights from new data remains a major challenge. We introduce knowledge-enhanced phylogenetic problems for both supertree and supermatrix phylogenetic analyses. These problems seek an optimal phylogenetic tree that can only be assembled from a user-supplied set of, possibly incompatible, phylogenetic relationships. We describe exact polynomial time algorithms for the knowledge-enhanced versions of the NP-hard Robinson Foulds, gene duplication, duplication and loss, and deep coalescence supertree problems. Further, we demonstrate that our algorithms can rapidly improve upon results of local search heuristics for these problems. Finally, we introduce a knowledge-enhanced search heuristic that can be applied to any discrete character data set using the maximum parsimony (MP) phylogenetic problem. Although this approach is not guaranteed to find exact solutions, we show that it also can improve upon solutions from commonly used MP heuristics.