ArticleGenome biology2023
CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer.Cancer research · 2026Article
- Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.Statistics in biosciences · 2026Article
- Genome doubling as a dynamic driver of ovarian cancer evolution: insights from single-cell sequencing.Journal of ovarian research · 2025Review
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.Genome biology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.
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Registered trials
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