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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Published on: August 14, 2018

Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies.

Nick S Jones1, John Moriarty

  • 1Department of Mathematics, Imperial College London, London, UK.

Journal of the Royal Society, Interface
|November 9, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical model for functional biological data, integrating phylogenetic relationships with Gaussian processes. This approach enhances predictions of ancestral functions and aids in evolutionary model selection.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Statistical Modeling

Background:

  • Biological data often represent functions, not simple values.
  • Phylogenetic relationships introduce correlations in biological data.

Purpose of the Study:

  • To develop a flexible statistical model for functional biological data with phylogenetic correlations.
  • To apply this model as a non-parametric Bayesian prior for prediction and model selection.

Main Methods:

  • Combining phylogenetic assumptions with Gaussian processes.
  • Extending Brownian motion and Ornstein-Uhlenbeck models to functional data.
  • Applying Bayesian inference for prediction and model comparison.

Main Results:

  • Developed a novel Bayesian framework for functional phylogenetic data.
  • Demonstrated utility in predicting ancestral functions.
  • Enabled comparison of evolutionary rates and phylogenetic model selection.

Conclusions:

  • The proposed model offers a flexible and integrative approach for analyzing complex biological data.
  • This method advances functional data analysis in phylogenetics and Bayesian statistics.