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Automatic Discovery of Optimal Discrete Character Models.

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  • 1Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan 48109, USA.

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This study introduces automatic model selection and regularization for discrete character evolution, significantly reducing parameter error and improving phylogenetic comparative methods. These advancements offer more robust and realistic evolutionary inferences.

Keywords:
Ancestral state reconstructionBias-variance trade-offDiscrete character evolutionMachine learningPhylogenetic comparative methodsRegularizationhidden Markov model

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Area of Science:

  • Evolutionary Biology
  • Phylogenetics
  • Computational Biology

Background:

  • Phylogenetic comparative methods commonly use Markovian models for discrete character evolution.
  • Increasing model complexity with more taxa and characters leads to numerous model structures and parameters, hindering comprehensive analysis.

Purpose of the Study:

  • To develop a framework for automatic model selection and optimization in discrete character evolution models.
  • To address challenges of over-parameterization and explore a wider range of model structures.

Main Methods:

  • Application of regularization and simulated annealing for automatic model searching and optimization.
  • Testing the framework using simulation scenarios with hidden rates and multiple discrete characters.
  • Utilizing the dredge algorithm for model selection in ancestral state reconstruction.

Main Results:

  • Regularized models significantly outperform traditional approaches, showing lower variance and reduced parameter estimation error.
  • Automatic model selection identified a novel model structure with superior statistical performance.
  • The new model structure yielded different ancestral state reconstructions compared to default models.

Conclusions:

  • Automatic model selection combined with regularization overcomes over-parameterization issues in phylogenetic models.
  • Relying on default model sets can be problematic; exploring a larger model space enhances statistical robustness.
  • These methods provide more statistically robust and biologically realistic inferences in evolutionary studies.