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Fast and flexible Bayesian phylogenetics via modern machine learning

通过现代机器学习快速灵活的贝叶斯系统发育学

基本信息

批准号：
10654594
负责人：
Frederick Albert Matsen
金额：
$ 74.48万
依托单位：
FRED HUTCHINSON CANCER CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10654594
关键词：
Algorithms COVID-19 pandemic Code Collection Complex Computational Biology Custom Data Data Set Diagnostic Discipline Disease Disease Outbreaks Epidemic Exclusion Foundations Friends Functional disorder Genome Graph Heterogeneity Learning Libraries Location Machine Learning Markov chain Monte Carlo methodology Mathematics Methods Modeling Modernization Modification Nature Neural Network Simulation Pattern Phylogenetic Analysis Predisposition Procedures Public Health Research Personnel Sampling Statistical Models Structure Technology TensorFlow Time Training Trees Uncertainty Update Viral Viral Genome Work Writing cloud based data integration data modeling epidemiologic data flexibility genomic data high dimensionality insight knowledgebase machine learning framework marginalization mathematical methods migration neural network novel strategies open source pathogen prevent scale up theories tool transmission process user-friendly viral epidemic viral genomics viral transmission

项目摘要

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

项目摘要/总结