论文标题
EVOVGM：用于进化参数估计的深层生成模型
EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation
论文作者
论文摘要
大多数面向进化的深层生成模型并未明确考虑生物序列的基本进化动力学，因为它是在贝叶斯系统发育推理框架内进行的。在这项研究中，我们提出了一种深层变异贝叶斯生成模型（EVOVGM）的方法，该方法共同近似局部进化参数的真实后验并生成序列比对。此外，它是针对连续时间马尔可夫链替代模型（例如JC69，K80和GTR）进行实例化和调整的。我们通过低变异的随机估计器和梯度上升算法训练模型。在这里，我们分析了VOVGM对模拟几种进化场景和不同大小的合成序列比对的一致性和有效性。最后，我们使用冠状病毒基因的序列比对来强调微调evoVGM模型的鲁棒性。
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.
