Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks

TL;DR

This paper introduces a learnable topological feature method for phylogenetic inference that leverages graph neural networks to adaptively capture structural information, reducing the need for domain expertise.

Contribution

The paper presents a novel approach combining Dirichlet energy minimization with graph learning to automatically adapt topological features for various phylogenetic inference tasks.

Findings

Effective on simulated data for tree probability estimation

Performs well on real variational Bayesian phylogenetic inference problems

Reduces manual design effort in topological feature selection

Abstract

Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort and domain expertise. In this paper, we propose a novel structural representation method for phylogenetic inference based on learnable topological features. By combining the raw node features that minimize the Dirichlet energy with modern graph representation learning techniques, our learnable topological features can provide efficient structural information of phylogenetic trees that automatically adapts to different downstream tasks without requiring domain expertise. We demonstrate the effectiveness and efficiency of our method on a simulated data tree probability estimation task and a benchmark of challenging real data variational Bayesian phylogenetic inference problems.