Paper Info
Title
The Cluster Affinity Distance for Phylogenies.
Abstract
Studying phylogenetic trees is fundamental to biology and benefitting a vast variety of other research areas. Comparing such trees is essential to such studies for which a growing and diverse collection of tree distances are available. In practice, tree distances suffer from problems that can severely limit their applicability. Notably, these distances include the cluster matching distance that is adapted from the Robinson-Foulds distance to overcome many of the drawbacks of this traditional measure. However, at the same time, the cluster matching distance is much more confined in its application than the Robinson-Foulds distance and makes sacrifices for satisfying the properties of a metric. Here, we propose the cluster affinity distance, a new tree distance that is adapted from the cluster matching distance but has not its drawbacks. Nevertheless, as we show, the cluster affinity distance preserves all of the properties that make the matching distance appealing.
Year
DOI
Venue
2019
10.1007/978-3-030-20242-2_5
BIOINFORMATICS RESEARCH AND APPLICATIONS, ISBRA 2019
Keywords
Field
DocType
Phylogenies,Cluster matching distance,Cluster affinity distance
Phylogenetic tree,Computer science,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
11490
0302-9743
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jucheol Moon1153.63
Oliver Eulenstein250552.71