On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks

TL;DR

This paper demonstrates that minimal neural networks can efficiently approximate traditional phylogenetic distance functions, offering scalable and accurate alternatives to complex model-based methods in evolutionary biology.

Contribution

It introduces simple neural network architectures capable of approximating phylogenetic distances, providing scalable and computationally efficient tools for evolutionary analysis.

Findings

Neural networks can approximate classic phylogenetic distance functions.

The proposed architectures are scalable to large datasets.

Learned distances generalize well and match state-of-the-art methods.

Abstract

Inferring the phylogenetic relationships among a sample of organisms is a fundamental problem in modern biology. While distance-based hierarchical clustering algorithms achieved early success on this task, these have been supplanted by Bayesian and maximum likelihood search procedures based on complex models of molecular evolution. In this work we describe minimal neural network architectures that can approximate classic phylogenetic distance functions and the properties required to learn distances under a variety of molecular evolutionary models. In contrast to model-based inference (and recently proposed model-free convolutional and transformer networks), these architectures have a small computational footprint and are scalable to large numbers of taxa and molecular characters. The learned distance functions generalize well and, given an appropriate training dataset, achieve results comparable to state-of-the art inference methods.