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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Mathieu Fourment¹, Christiaan J Swanepoel^2,3, Jared G Galloway⁴

¹Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW, Australia.

Genome Biology and Evolution

|June 2, 2023

Summary

This summary is machine-generated.

Automatic differentiation is slower than specialized methods for phylogenetic likelihood gradients. A combined approach using both phylogenetic and machine learning libraries offers the best performance and flexibility.

Keywords:

Bayesian inference gradient phylogenetics variational inference

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Published on: August 16, 2017

Area of Science:

Computational Statistics
Machine Learning
Phylogenetics

Background:

Gradients of probabilistic model likelihoods are crucial for computational statistics and machine learning.
Automatic differentiation (AD) in libraries like TensorFlow and PyTorch offers general gradient computation.
The efficiency of AD for phylogenetics-specific tasks compared to specialized code is not well-established.

Purpose of the Study:

To compare the performance of automatic differentiation with phylogenetics-specific gradient implementations.
To evaluate gradient calculation speed and scalability in the context of phylogenetic likelihood functions.
To assess the impact of gradient computation on variational inference procedures in phylogenetics.

Main Methods:

Implemented and compared six different gradient calculation methods for phylogenetic likelihood functions.
Evaluated implementations both in isolation and within a variational inference framework.
Assessed performance based on computational speed and scalability with respect to tree size.

Main Results:

Automatic differentiation scales approximately linearly with tree size.
Specialized gradient calculations for tree likelihood and ratio transformations are significantly faster than AD.
AD's general-purpose nature results in slower performance for these specific phylogenetic operations.

Conclusions:

A hybrid approach, combining specialized phylogenetic libraries with general machine learning libraries, is recommended.
This mixed strategy balances computational speed with the flexibility needed for complex phylogenetic modeling.
Future advancements should leverage the strengths of both specialized and general-purpose tools.