PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies

Phylodynamics and diversification studies using complex evolutionary models can be challenging, especially with traditional likelihood-based approaches. As an alternative, likelihood-free simulation-based approaches have been proposed due to their ability to incorporate complex models and scenarios. Here, we propose a new simulation-based deep learning (DL) method capable of selecting birth-death models and accurately estimating their parameters in both phylodynamics and diversification studies. We use a convolutional approach, where trees are encoded using the neighborhood of all nodes and leaves of the input phylogeny. We also developed a dedicated neural network architecture called PhyloCNN. Using simulations, we compared the accuracy of PhyloCNN when using a variable number of neighbors to describe the local context of nodes and leaves. The number of neighbors had a greater impact when considering smaller training sets, with a broader context showing higher accuracy, especially for ..., , , # PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies

Dryad DOI: https://doi.org/10.5061/dryad.prr4xgxx9

This folder contains the code and the two empirical phylogenies analyzed in the manuscript entitled \"PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies\", by Manolo Perez and Olivier Gascuel. These two phylogenies are in Newick format.

**PhyloCNN_GitHub_12Nov2025.zip**: This file contains scripts and notebooks to perform simulations, encoding, model selection, parameter estimation, and posterior distribution analyses using PhyloCNN. This file is a mirror of phyloCNN's GitHub ([https://github.com/manolofperez/phyloCNN/](https://github.com/manolofperez/phyloCNN/)) on 14 October 2025.

**ZurichHIV-tree.nwk**: 200 taxa, from (Rasmussen et al. PLOS CB, 2017), analyzed in (Voznica et al. Nature Com 202...,