Name
Abstract | ||
|---|---|---|
Inferring phylogenetic relationships between sequences is a difficult and interesting problem. Assuming that there is enough phylogenetic signal in biological sequence to resolve every tree bifurcation, the resulting tree is a representation of the vertical descent history of a gene. A popular method to evaluate a candidate phylogenetic tree uses the likelihood of the data, given an empirical model of character substitution. The computational cost of search for the maximum-likelihood tree is, however, very large. In this paper, we present an algorithm for protein phylogeny using a maximum likelihood framework. A key design goal, which differentiates our method from others, is that it determines a range (confidence set) of statistically equivalent trees, instead of only a single solution. We also present a number of sequential algorithmic enhancements and both sequential and parallel performance results. |
Year | Venue | Keywords |
|---|---|---|
2005 | ISCA PDCS | maximum likelihood methods,protein phylogeny,parallel computing.,maximum likelihood method,parallel computer,phylogenetic tree,maximum likelihood,empirical model |
Field | DocType | Citations |
Phylogenetic tree,Tree rearrangement,Pattern recognition,Maximum likelihood,Computational phylogenetics,Artificial intelligence,Phylogenetics,Mathematics,Bifurcation | Conference | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Blouin | 1 | 85 | 7.57 |
| Davin Butt | 2 | 33 | 1.97 |
| Glenn Hickey | 3 | 6 | 2.33 |
| Andrew Rau-chaplin | 4 | 638 | 61.65 |