Fast and flexible Bayesian phylogenetics via modern machine learning

项目摘要/摘要 SARS-CoV-2大流行突显了我们对全球病原体暴发的敏感性和死亡人数。 病毒基因组的系统发育分析为了解疾病的病理生理、传播和传播提供了关键的洞察力。 情绪控制。然而，如果要在病毒控制策略中使用这些方法，它们必须可靠地说明 不确定，并能够在可行的时间内对1,000个基因组进行推断。比例尺贝叶斯系统发育图- 满足这一需求的ICS是一个巨大的挑战，不太可能通过优化现有算法来满足。 我们将用一种全新的方法来应对这一挑战：对系统发育的贝叶斯变分推理-- 使用fl可扩展分布的ICS(Vip)在使用基于梯度的方法类似的fit的系统发育树上 一位Effi如何科学地训练大量的神经网络。通过采用变分方法，我们还将能够 将系统进化分析集成到非常强大的开源建模框架中，如TensorFlow 还有火炬。这将开辟新的模型类别，如神经网络模型，以集成这样的数据 作为采样位置和迁徙模式与系统发育推论。这些fl可扩展模型将通知 病毒控制的策略。 在目标1中，我们将发展可扩展和可靠的VIP所需的理论，包括子树边缘- 在线算法所需的局部梯度更新、收敛诊断和参数支持 估计。我们将在VIP的C++基础库中实现这些算法。在《目标2》中我们将 开发一个基于fl可伸缩TensorFlow的系统发育建模平台，使 基于神经网络的系统发育模型用最小程序学习系统动力学异质性 明朝的努力。我们将通过我们的C++库为这个实现提供fi有效的渐变。在《目标3》中，我们将 利用VIP后方是耐用的和可扩展的完整数据后方描述这一事实来实现 动态在线计算变异后继者，包括分而治之的贝叶斯系统发育学。 这项工作将使基于云的病毒系统发育解决方案能够快速更新我们目前对 当新数据到达或模型被修改时的后验分布。 1

Project Abstract/Summary The SARS-CoV-2 pandemic underlines both our susceptibility to and the toll of a global pathogen outbreak. Phylogenetic analysis of viral genomes provides key insight into disease pathophysiology, spread and po- tential control. However, if these methods are to be used in a viral control strategy they must reliably account for uncertainty and be able to perform inference on 1,000s of genomes in actionable time. Scaling Bayesian phylogenet- ics to meet this need is a grand challenge that is unlikely to be met by optimizing existing algorithms. We will meet this challenge with a radically new approach: Bayesian variational inference for phylogenet- ics (VIP) using flexible distributions on phylogenetic trees that are fit using gradient-based methods analogous to how one efficiently trains massive neural networks. By taking a variational approach we will also be able to integrate phylogenetic analysis into very powerful open-source modeling frameworks such as TensorFlow and PyTorch. This will open up new classes of models, such as neural network models, to integrate data such as sampling location and migration patterns with phylogenetic inference. These flexible models will inform strategies for viral control. In Aim 1 we will develop the theory necessary for scalable and reliable VIP, including subtree marginal- ization, local gradient updates needed for online algorithms, convergence diagnostics, and parameter support estimates. We will implement these algorithms in our C++ foundation library for VIP. In Aim 2 we will develop a flexible TensorFlow-based modeling platform for phylogenetics, enabling a whole new realm of phylogenetic models based on neural networks to learn phylodynamic heterogeneity with minimal program- ming effort. We will provide efficient gradients to this implementation via our C++ library. In Aim 3 we will use the fact that VIP posteriors are durable and extensible descriptions of the full data posterior to enable dynamic online computation of variational posteriors, including divide-and-conquer Bayesian phylogenetics. This work will enable a cloud-based viral phylogenetics solution to rapidly update our current estimate of the posterior distribution when new data arrive or the model is modified. 1