ArticleAlgorithms for molecular biology : AMB2019
A multi-labeled tree dissimilarity measure for comparing "clonal trees" of tumor progression.
Article in Algorithms for molecular biology : AMB, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment.bioRxiv : the preprint server for biology · 2026Article
- Inferring and summarizing tumor phylogenies from bulk DNA data.Algorithms for molecular biology : AMB · 2026Article
- Weighted centroid trees: a general approach to summarize phylogenies in single-labeled tumor mutation tree inference.Bioinformatics (Oxford, England) · 2024Article
- TheJournal of computational biology : a journal of computational molecular cell biology · 2024Article
- A weighted distance-based approach for deriving consensus tumor evolutionary trees.Bioinformatics (Oxford, England) · 2023Article
- The Generalized Robinson-Foulds Distance for Phylogenetic Trees.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data.BMC bioinformatics · 2021Article
- The Bourque distances for mutation trees of cancers.Algorithms for molecular biology : AMB · 2021Article
- Triplet-based similarity score for fully multilabeled trees with poly-occurring labels.Bioinformatics (Oxford, England) · 2021Article
- Tumor Phylogeny Topology Inference via Deep Learning.iScience · 2020Article
- Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm.BMC bioinformatics · 2020Article
- PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem.Bioinformatics (Oxford, England) · 2020Article
- PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data.Genome research · 2019Article
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4 authors.
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Abstract
We introduce a new dissimilarity measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well.
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