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A machine-learning-based alternative to phylogenetic bootstrap.

Noa Ecker1, Dorothée Huchon2,3, Yishay Mansour4

  • 1The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.

Bioinformatics (Oxford, England)
|June 28, 2024
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Summary
This summary is machine-generated.

This study introduces a novel machine learning approach for estimating phylogenetic branch support, offering faster and more accurate probability-based values than traditional methods like Felsenstein's bootstrap.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Machine Learning

Background:

  • Estimating branch support in phylogenetic analyses is crucial for understanding evolutionary relationships.
  • Current methods, such as Felsenstein's bootstrap, have limitations in speed and interpretability.

Purpose of the Study:

  • To develop a data-driven approach for accurate and fast estimation of probability-based branch support values.
  • To improve upon existing methods for assessing the reliability of phylogenetic trees.

Main Methods:

  • Simulated thousands of realistic phylogenetic trees and corresponding multiple sequence alignments.
  • Inferred phylogenies using state-of-the-art software and compared them to true trees.
  • Trained machine learning algorithms on simulated data to predict branch support values for maximum-likelihood trees.

Main Results:

  • The developed machine learning model provides faster and more accurate probability-based branch support values compared to commonly used procedures.
  • The approach demonstrates applicability on empirical datasets.

Conclusions:

  • The novel machine learning approach offers a significant advancement in estimating phylogenetic branch support.
  • This method enhances the accuracy and speed of phylogenetic tree interpretation.