CherryML: scalable maximum likelihood estimation of phylogenetic models

2023/06/29 by Sebastian Prillo, Yun Deng, Pierre Boyeau +4 · 1 voice · 4 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Mathematics · #Evolution and Paleontology Studies #Genomics and Phylogenetic Studies #Morphological variations and asymmetry

paper · doi:10.1038/s41592-023-01917-9

crossref issued 2023/06/29 · crossref published 2023/06/29 · crossref published-online 2023/06/29 · openalex publication_date 2023/06/29 · crossref created 2023/06/29 · crossref published-print 2023/08/01 · crossref deposited 2023/08/07 · openalex created_date 2025/10/10 · crossref indexed 2026/05/27 · openalex updated_date 2026/07/23

Citations

• Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods
• Improved protein structure prediction using predicted interresidue orientations
• IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era
• Highly accurate protein structure prediction with AlphaFold

Cited by

• Likelihood-free inference of phylogenetic tree posterior distributions
• Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks
• The promise of composite likelihood for species-level phylogenomic inference
• Phylogenetic Methods Meet Deep Learning

Discussions

• Last year, we introduced a novel framework called CherryML (doi.org/10.1038/s415...) to accelerate maximum likelihood estimation of phylogenetic models, achieving massive speedup. However, CherryML st [bsky, 4 points, 1 comments]