GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies

TL;DR

GeoPhy introduces a fully differentiable approach to phylogenetic inference that models tree topologies in continuous geometric spaces, enabling scalable Bayesian analysis without restricting candidate trees.

Contribution

The paper presents GeoPhy, a novel method that allows variational Bayesian phylogenetic inference in continuous spaces, overcoming the combinatorial complexity of tree topologies.

Findings

Outperforms existing Bayesian methods on benchmark datasets

Enables scalable inference without restricting topological candidates

Provides a differentiable framework for phylogenetic analysis

Abstract

Phylogenetic inference, grounded in molecular evolution models, is essential for understanding the evolutionary relationships in biological data. Accounting for the uncertainty of phylogenetic tree variables, which include tree topologies and evolutionary distances on branches, is crucial for accurately inferring species relationships from molecular data and tasks requiring variable marginalization. Variational Bayesian methods are key to developing scalable, practical models; however, it remains challenging to conduct phylogenetic inference without restricting the combinatorially vast number of possible tree topologies. In this work, we introduce a novel, fully differentiable formulation of phylogenetic inference that leverages a unique representation of topological distributions in continuous geometric spaces. Through practical considerations on design spaces and control variates for gradient estimations, our approach, GeoPhy, enables variational inference without limiting the topological candidates. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.