Name
Abstract | ||
|---|---|---|
Supertree problems are a standard tool for synthesizing large-scale species trees from a given collection of gene trees under some problem-specific objective. Unfortunately, these problems are typically NP-hard, and often remain so when their instances are restricted to rooted gene trees sampled from the same species. While a class of restricted supertree problems has been effectively addressed by the parameterized strict consensus approach, in practice, most gene trees are unrooted and sampled from different species. Here, we overcome this stringent limitation by describing efficient algorithms that are adopting the strict consensus approach to also handle unrestricted supertree problems. Finally, we demonstrate the performance of our algorithms in a comparative study with classic supertree heuristics using simulated and empirical data sets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1142/S0219720017400029 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
Phylogenetics,Pareto for clusters,guidance tree,strict consensus approach | Parameterized complexity,Data set,Biology,Supertree,Heuristics,Artificial intelligence,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
15 | SP3 | 0219-7200 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jucheol Moon | 1 | 15 | 3.63 |
| Oliver Eulenstein | 2 | 505 | 52.71 |