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arXiv · 2205.13034

EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation

Amine M. Remita·Abdoulaye Baniré Diallo
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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.

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Amine M. Remita, Abdoulaye Baniré Diallo. 2022-07-01. EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation. https://doi.org/10.1145/3535508.3545563

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