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Ancestral State Reconstruction With Discrete Characters Using Deep Learning.

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Ancestral State Reconstruction With Discrete Characters Using Deep Learning.

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Ancestral state reconstruction with discrete characters using deep learning.

Anna A Nagel¹, Michael J Landis¹

¹One Brookings Drive, Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA.

Biorxiv : the Preprint Server for Biology

|March 27, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Deep learning software phyddle reconstructs ancestral states in phylogenetics, offering an alternative for complex models where traditional methods fail. Performance is comparable to Bayesian inference for simple models but decreases with tree size.

Keywords:

ancestral state reconstruction deep learning discrete characters phylogenetics statistical models

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Area of Science:

Phylogenetics
Computational Biology
Machine Learning

Background:

Ancestral state reconstruction is crucial in phylogenetics.
Likelihood-based methods are limited by tractable likelihood functions.
Complex, biologically realistic models often have intractable likelihoods.

Purpose of the Study:

To adapt the deep learning software phyddle for ancestral state reconstruction.
To evaluate phyddle's performance on various models and tree sizes.
To compare phyddle with Bayesian inference for ancestral state reconstruction.

Main Methods:

Modification of the phyddle software for ancestral state reconstruction.
Performance evaluation under diverse methodological and modeling conditions.

Comparison with Bayesian inference where feasible.

Main Results:

Phyddle performance mirrors Bayesian inference for simple models and small trees.
Performance degrades as phylogenetic tree size increases.
Phyddle adequately handles complex models (e.g., speciation/extinction) but shows greater divergence from Bayesian inference.

Conclusions:

Phyddle provides a viable deep learning approach for ancestral state reconstruction, especially for models with intractable likelihoods.
The method shows promise for empirical datasets, including genus Liolaemus and Ebola virus evolution.
Further research may be needed to optimize performance for large phylogenetic trees.