EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation
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 (EvoVGM) that jointly approximates the true posterior of local evolutionary parameters and generates sequence alignments. Moreover, it is instantiated and tuned for continuous-time Markov chain substitution models such as JC69, K80 and GTR. We train the model via a low-variance stochastic estimator and a gradient ascent algorithm. Here, we analyze the consistency and effectiveness of EvoVGM on synthetic sequence alignments simulated with several evolutionary scenarios and different sizes. Finally, we highlight the robustness of a fine-tuned EvoVGM model using a sequence alignment of gene S of coronaviruses.
Cite
@article{arxiv.2205.13034,
title = {EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation},
author = {Amine M. Remita and Abdoulaye Baniré Diallo},
journal= {arXiv preprint arXiv:2205.13034},
year = {2022}
}
Comments
Accepted as a full paper for publication in ACM-BCB 2022 (Camera-ready version)