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Jose Castresana

dblp:29/2537 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
phylogenetics
0.112007
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
0.112007
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
0.012007
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
YearPublicationVenuePosition
2007 The K tree score: quantification of differences in the relative branch length and topology of phylogenetic trees
abstract
SUMMARY: 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