5 research outputs found
Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference
The advances in variational inference are providing promising paths in
Bayesian estimation problems. These advances make variational phylogenetic
inference an alternative approach to Markov Chain Monte Carlo methods for
approximating the phylogenetic posterior. However, one of the main drawbacks of
such approaches is the modelling of the prior through fixed distributions,
which could bias the posterior approximation if they are distant from the
current data distribution. In this paper, we propose an approach and an
implementation framework to relax the rigidity of the prior densities by
learning their parameters using a gradient-based method and a neural
network-based parameterization. We applied this approach for branch lengths and
evolutionary parameters estimation under several Markov chain substitution
models. The results of performed simulations show that the approach is powerful
in estimating branch lengths and evolutionary model parameters. They also show
that a flexible prior model could provide better results than a predefined
prior model. Finally, the results highlight that using neural networks improves
the initialization of the optimization of the prior density parameters.Comment: Accepted as a full paper for publication at RECOMB-CG 2023
(Camera-ready version). 15 pages (excluding references), 6 tables and 1
figur
EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation
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.Comment: Accepted as a full paper for publication in ACM-BCB 2022
(Camera-ready version