Bayesian Machine Learning Tools for Analyzing Microbiome Dynamics

用于分析微生物组动力学的贝叶斯机器学习工具

• 批准号: 10245080
• 负责人: Georg Kurt Gerber
• 金额: $ 31.29万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2023-08-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10245080
• 关键词: 16S ribosomal RNA sequencing; Affect; Algorithms; Antibiotics; Arthritis; Autoimmunity; Bayesian learning; Behavior; Biological Markers; Biological Models; Brain Diseases; Cardiovascular Diseases; Childhood; Clostridium difficile; Collection; Communities; Complex; Computer software; Computers; Data; Data Set; Dependence; Diabetes Mellitus; Diet; Disease; Engineering; Environmental Exposure; Frequencies; Funding; Health; Heart Diseases; Human; Human Microbiome; Hypersensitivity; Infection; Institutes; Intervention; Intestines; Investigation; Kidney Diseases; Knowledge; Learning; Link; Longitudinal Studies; Malignant Neoplasms; Measurement; Medical; Metagenomics; Microbe; Modeling; Names; Noise; Oligosaccharides; Outcome; Pattern; Performance; Phylogenetic Analysis; Play; Population Dynamics; Property; Pythons; Readability; Research Design; Research Personnel; Role; Sampling; Scientist; Series; Shotguns; Software Engineering; Software Tools; Speed; Structure; Testing; Therapeutic; Time; Time Series Analysis; United States National Institutes of Health; Work; base; computerized tools; design; dietary; disorder risk; dynamic system; gut microbiota; human data; human microbiota; human subject; implementation tool; improved; insight; learning algorithm; machine learning method; man; metabolomics; metagenome; microbial; microbiome; microbiome analysis; microbiome sequencing; microbiota; microorganism; nervous system disorder; novel; open source; pathogen; predictive tools; prevent; recurrent infection; response; software development; targeted treatment; tool

The human microbiota plays an important role in health and disease, and its therapeutic manipulation is being actively investigated for a wide range of diseases that span every NIH institute. Our microbiota are inherently dynamic, and analyzing these time-dependent properties is key to robustly linking the microbiota to disease, and predicting the effects of therapies targeting the microbiota; indeed, longitudinal microbiome data is being acquired with increasing frequency, and is a major component of many NIH-funded projects. However, there is currently a dearth of computational tools for analyzing microbiome time-series data, which presents several special challenges including high measurement noise, irregular and sparse temporal sampling, and complex dependencies between variables. The objective of this proposal is to introduce new capabilities, improve on, and provide state-of-the-art implementations of tools for analyzing dynamics, or patterns of change in microbiome time-series data. The tools we develop will use Bayesian machine learning methods, which are well-recognized for their strong conceptual and practical advantages, particularly in biomedical domains. Tools will be rigorously tested and validated on synthetic and real human microbiome data, including publicly available datasets and those from collaborators providing 16S rRNA sequencing, metagenomic, and metabolomics data. We propose three specific aims. For Aim 1, we will develop integrated Bayesian machine learning tools for predicting population dynamics of the microbiome and its responses to perturbations. These tools will include a new model that simultaneously learns groups of microbes with similar interaction structure and predicts their behavior over time, and that incorporates prior phylogenetic information. The model will be further improved by incorporating stochastic microbial dynamics and errors in measurements throughout the model. For Aim 2, we will develop Bayesian machine learning tools to predict host status from microbiome dynamics. The tools will learn easily interpretable, human-readable rules that predict host status from microbiome time-series data, and will be further extended to handle a variety of longitudinal study designs. For Aim 3, we will engineer our microbiome dynamics analysis software tools for optimal performance, ease-of- use, maintainability, extensibility, and dissemination to the community. In total, the proposed work will yield a suite of contemporary software tools for analyzing microbiome dynamics, with expected broad use and major impact. The software will allow investigators to answer important scientific and translational questions about the microbiome, including discovering which microbial taxa or their metagenomes are affected over time by perturbations such as changes in diet or invasion by pathogens; predicting the effects of these perturbations over time, including changes in composition or stability of the gut microbiota; and finding temporal signatures in multi-‘omic microbiome data that predict disease risk in the human host.

人类微生物群在健康和疾病中发挥着重要作用，其治疗方法正在被研究 积极研究跨越每个 NIH 研究所的各种疾病。我们的微生物群本质上是 动态，分析这些时间依赖性特性是将微生物群与疾病牢固联系起来的关键， 并预测针对微生物群的疗法的效果；事实上，纵向微生物组数据正在被 获得的频率越来越高，并且是许多 NIH 资助项目的主要组成部分。然而，有 目前缺乏用于分析微生物组时间序列数据的计算工具，这提出了几种 特殊的挑战包括高测量噪声、不规则和稀疏的时间采样以及复杂的 变量之间的依赖关系。该提案的目标是引入新功能、改进、 并提供最先进的工具实现来分析动态或变化模式 微生物组时间序列数据。我们开发的工具将使用贝叶斯机器学习方法，即 因其强大的概念和实践优势而受到广泛认可，特别是在生物医学领域。工具 将根据合成和真实的人类微生物组数据进行严格的测试和验证，包括公开的 可用的数据集以及来自提供 16S rRNA 测序、宏基因组和 代谢组学数据。我们提出三个具体目标。对于目标 1，我们将开发集成贝叶斯机 用于预测微生物群的种群动态及其对扰动的反应的学习工具。这些 工具将包括一个新模型，该模型可以同时学习具有相似相互作用结构的微生物群 并预测它们随着时间的推移的行为，并且结合了先前的系统发育信息。该模型将是 通过在整个过程中纳入随机微生物动力学和测量误差来进一步改进 模型。对于目标 2，我们将开发贝叶斯机器学习工具来根据微生物组预测宿主状态 动力学。这些工具将学习易于解释、人类可读的规则，从而预测主机状态 微生物组时间序列数据，并将进一步扩展以处理各种纵向研究设计。为了 目标 3，我们将设计我们的微生物组动力学分析软件工具，以实现最佳性能、易于操作 使用、可维护性、可扩展性和向社区传播。总的来说，拟议的工作将产生 用于分析微生物组动态的现代软件工具套件，具有预期的广泛用途和主要用途 影响。该软件将使研究人员能够回答有关以下方面的重要科学和转化问题： 微生物组，包括发现哪些微生物类群或其宏基因组随着时间的推移受到影响 饮食变化或病原体入侵等干扰；预测这些扰动的影响 随着时间的推移，包括肠道微生物群的组成或稳定性的变化；并找到时间签名 预测人类宿主疾病风险的多组学微生物组数据。