Demo Data from EDBT 2026 / reviewers in the wild / expert
Jose Castresana
dblp:29/2537
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2007
0000-0002-9391-8050ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
phylogenetics |
0.1 | 1 | 2007 | The K tree score: quantification of differences in the relative branch length and topology of phylogenetic trees · Bioinform. 2007 |
Bioinformatics and computational biology › phylogenetics
phylogenetic tree comparison |
0.1 | 1 | 2007 | The K tree score: quantification of differences in the relative branch length and topology of phylogenetic trees · Bioinform. 2007 |
Bioinformatics and computational biology › comparative genomics › orthology analysis
ortholog identification |
0.0 | 1 | 2007 | The K tree score: quantification of differences in the relative branch length and topology of phylogenetic trees · Bioinform. 2007 |
Methods — techniques the papers use, named apart from their topics
tree scaling · 0.1branch length distance · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | The K tree score: quantification of differences in the relative branch length and topology of phylogenetic treesabstractSUMMARY: We introduce a new phylogenetic comparison method that measures overall differences in the relative branch length and topology of two phylogenetic trees. To do this, the algorithm first scales one of the trees to have a global divergence as similar as possible to the other tree. Then, the branch length distance, which takes differences in topology and branch lengths into account, is applied to the two trees. We thus obtain the minimum branch length distance or K tree score. Two trees with very different relative branch lengths get a high K score whereas two trees that follow a similar among-lineage rate variation get a low score, regardless of the overall rates in both trees. There are several applications of the K tree score, two of which are explained here in more detail. First, this score allows the evaluation of the performance of phylogenetic algorithms, not only with respect to their topological accuracy, but also with respect to the reproduction of a given branch length variation. In a second example, we show how the K score allows the selection of orthologous genes by choosing those that better follow the overall shape of a given reference tree. AVAILABILITY: http://molevol.ibmb.csic.es/Ktreedist.html Víctor Soria-Carrasco, Gerard Talavera, Javier Igea, Jose Castresana |
Bioinform. | 4 |