CPTAM: Constituency Parse Tree Aggregation Method

Kulkarni, Adithya; Sabetpour, Nasim; Маркин, А. В.; Eulenstein, Oliver; Li, Qi

doi:10.1137/1.9781611977172.71

Cited by 2 publications

(1 citation statement)

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“…Therefore, developing tree comparison costs with beneficial characteristics will enhance research in phylogenetics and other areas that rely upon tree-focused inference. For example, comparison costs are used in epidemiology for comparing transmission trees and virus genealogies (Giardina et al, 2017 ), to determine horizontal gene transfer events (Bogdanowicz and Giaro, 2017 ), and in natural language processing to compare and determine an aggregate parse tree (Kulkarni et al, 2022 ). Consequently, the development and improvement of tree comparison costs is a mature and highly active research area in computational and comparative phylogenetics (e.g., Robinson et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Cluster-Based Measures for Comparative Phylogenetics

Wagle,

Markin,

Górecki

et al. 2024

Journal of Computational Biology

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Phylogenetic inference and reconstruction methods generate hypotheses on evolutionary history. Competing inference methods are frequently used, and the evaluation of the generated hypotheses is achieved using tree comparison costs. The Robinson–Foulds (RF) distance is a widely used cost to compare the topology of two trees, but this cost is sensitive to tree error and can overestimate tree differences. To overcome this limitation, a refined version of the RF distance called the Cluster Affinity (CA) distance was introduced. However, CA distances are symmetric and cannot compare different types of trees. These asymmetric comparisons occur when gene trees are compared with species trees, when disparate datasets are integrated into a supertree, or when tree comparison measures are used to infer a phylogenetic network. In this study, we introduce a relaxation of the original Affinity distance to compare heterogeneous trees called the asymmetric CA cost. We also develop a biologically interpretable cost, the Cluster Support cost that normalizes by cluster size across gene trees. The characteristics of these costs are similar to the symmetric CA cost. We describe efficient algorithms, derive the exact diameters, and use these to standardize the cost to be applicable in practice. These costs provide objective, fine-scale, and biologically interpretable values that can assess differences and similarities between phylogenetic trees.

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Section: Introductionmentioning

confidence: 99%