PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation

TL;DR

PhyloGen introduces a novel approach that uses a pre-trained language model to generate and optimize phylogenetic trees, improving accuracy and efficiency over traditional methods by jointly modeling topology and branch lengths.

Contribution

It presents a new method that leverages language models for phylogenetic inference, avoiding reliance on evolutionary models and pre-generated topologies, and jointly optimizing tree structures and branch lengths.

Findings

Effective on eight real-world datasets

Provides deeper insights into phylogenetic relationships

Outperforms traditional inference methods

Abstract

Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens. Existing Variational Inference methods, which require pre-generated topologies and typically treat tree structures and branch lengths independently, may overlook critical sequence features, limiting their accuracy and flexibility. We propose PhyloGen, a novel method leveraging a pre-trained genomic language model to generate and optimize phylogenetic trees without dependence on evolutionary models or aligned sequence constraints. PhyloGen views phylogenetic inference as a conditionally constrained tree structure generation problem, jointly optimizing tree topology and branch lengths through three core modules: (i) Feature Extraction, (ii) PhyloTree Construction, and (iii) PhyloTree Structure Modeling. Meanwhile, we introduce a Scoring Function to guide the model towards a more stable gradient descent. We demonstrate the effectiveness and robustness of PhyloGen on eight real-world benchmark datasets. Visualization results confirm PhyloGen provides deeper insights into phylogenetic relationships.