EvoVGM: A Deep Variational Generative Model for Evolutionary Parameter
Estimation
- URL: http://arxiv.org/abs/2205.13034v1
- Date: Wed, 25 May 2022 20:08:10 GMT
- Title: EvoVGM: A Deep Variational Generative Model for Evolutionary Parameter
Estimation
- Authors: Amine M. Remita and Abdoulaye Banir\'e Diallo
- Abstract summary: We propose a method for a deep variational Bayesian generative model that jointly approximates the true posterior of local biological evolutionary parameters.
We show the consistency and effectiveness of the method on synthetic sequence alignments simulated with several evolutionary scenarios and on a real virus sequence alignment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Most evolutionary-oriented deep generative models do not explicitly consider
the underlying evolutionary dynamics of biological sequences as it is performed
within the Bayesian phylogenetic inference framework. In this study, we propose
a method for a deep variational Bayesian generative model that jointly
approximates the true posterior of local biological evolutionary parameters and
generates sequence alignments. Moreover, it is instantiated and tuned for
continuous-time Markov chain substitution models such as JC69 and GTR. We train
the model via a low-variance variational objective function and a gradient
ascent algorithm. Here, we show the consistency and effectiveness of the method
on synthetic sequence alignments simulated with several evolutionary scenarios
and on a real virus sequence alignment.
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