ArticleNature methods2023
CherryML: scalable maximum likelihood estimation of phylogenetic models.
Article in Nature methods, 2023. 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.
- Variational inference in coupled models of amino acid substitution.bioRxiv : the preprint server for biology · 2026Article
- Nested birth-death processes are competitive with parameter-heavy neural networks as time-dependent models of protein evolution.bioRxiv : the preprint server for biology · 2026Article
- Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins.bioRxiv : the preprint server for biology · 2026Article
- Viral Simulation Reveals Overestimation Bias in Within-Host Phylodynamic Migration Rate Estimates Under Selection.Molecular biology and evolution · 2026Article
- ConvexML: Fast and Accurate Branch Length Estimation under Irreversible Mutation Models, Illustrated through Applications to CRISPR/Cas9-Based Lineage Tracing.Systematic biology · 2026Article
- Integrating the Contrasting Perspectives Between the Constrained Disorder Principle and Deterministic Optical Nanoscopy: Enhancing Information Extraction from Imaging of Complex Systems.Bioengineering (Basel, Switzerland) · 2026Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- ConvexML: Fast and accurate branch length estimation under irreversible mutation models, illustrated through applications to CRISPR/Cas9-based lineage tracing.bioRxiv : the preprint server for biology · 2025Article
- Maximum likelihood inference of time-scaled cell lineage trees with mixed-type missing data using LAML.Genome biology · 2025Article
- Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.Molecular biology and evolution · 2025Article
- The chloroplast genome sequence and phylogenetic analysis of Rubia alata Wall and Rubia ovatifolia Z. Ying Zhang. (Rubiaceae).Molecular biology reports · 2024Article
- Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications.Molecular biology and evolution · 2024Review
- Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction.Advances in neural information processing systems · 2024Article
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Authors and funding
6 authors.
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Abstract
Phylogenetic models of molecular evolution are central to numerous biological applications spanning diverse timescales, from hundreds of millions of years involving orthologous proteins to just tens of days relating to single cells within an organism. A fundamental problem in these applications is estimating model parameters, for which maximum likelihood estimation is typically employed. Unfortunately, maximum likelihood estimation is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup by using a quantized composite likelihood over cherries in the trees. The massive speedup offered by our method should enable researchers to consider more complex and biologically realistic models than previously possible. Here we demonstrate CherryML's utility by applying it to estimate a general 400 × 400 rate matrix for residue-residue coevolution at contact sites in three-dimensional protein structures; we estimate that using current state-of-the-art methods such as the expectation-maximization algorithm for the same task would take >100,000 times longer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.