GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies
Abstract
Phylogenetic inference, grounded in molecular evolution models, is essential for understanding evolutionary relationships in biological data. While Variational Bayesian methods offer scalable models for biological analysis, reliable inference for latent tree topology and branch lengths remains challenging due to the vast possibilities for topological candidates. In response, we introduce GeoPhy, a novel approach that employs a fully differentiable formulation of phylogenetic inference, representing topological distributions in continuous geometric spaces without limiting topological candidates. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.
Cite
Text
Mimori and Hamada. "GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.Markdown
[Mimori and Hamada. "GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies." ICML 2023 Workshops: Differentiable_Almost_Everything, 2023.](https://mlanthology.org/icmlw/2023/mimori2023icmlw-geophy/)BibTeX
@inproceedings{mimori2023icmlw-geophy,
title = {{GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies}},
author = {Mimori, Takahiro and Hamada, Michiaki},
booktitle = {ICML 2023 Workshops: Differentiable_Almost_Everything},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/mimori2023icmlw-geophy/}
}