apTreeshape: statistical analysis of phylogenetic tree shape
Bioinformatics
A Fitness Distance Correlation Measure for Evolutionary Trees
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Faster computation of the Robinson-Foulds distance between phylogenetic networks
CPM'10 Proceedings of the 21st annual conference on Combinatorial pattern matching
A novel approach for compressing phylogenetic trees
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
Faster computation of the Robinson-Foulds distance between phylogenetic networks
Information Sciences: an International Journal
A fast algorithm for computing the quartet distance for large sets of evolutionary trees
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
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In this paper, we study two fast algorithms--HashRF and PGM-Hashed--for computing the Robinson-Foulds (RF) distance matrix between a collection of evolutionary trees. The RF distance matrix represents a tremendous data-mining opportunity for helping biologists understand the evolutionary relationships depicted among their trees. The novelty of our work results from using a variety of different architecture- and implementation-independent measures (i.e., percentage of bipartition sharing, number of bipartition comparisons, and memory usage) in addition to CPU time to explore practical algorithmic performance. Overall, our study concludes that HashRF performs better across the various performance measures than its competitor, PGM-Hashed. Thus, the HashRF algorithm provides scientists with a fast approach for understanding the evolutionary relationships among a set of trees.