Machine Learning for Integrative Modeling of the Immune System in Clinical Settings

临床环境中免疫系统综合建模的机器学习

• 批准号: 10433729
• 负责人: Nima Aghaeepour
• 金额: $ 9.61万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-05 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10433729
• 关键词: Accounting; Address; Algorithmic Analysis; Algorithms; Area; Biological; Biological Assay; Cells; Characteristics; Clinical; Collaborations; Complex; Computer software; Data; Data Set; Development; Diagnostic tests; Dimensions; Disease; Ensure; Environmental Risk Factor; Epidemiological trend; Foundations; Immune; Immune response; Immune system; Immunity; Immunodiagnostics; Immunologic Monitoring; Immunological Models; Immunologics; Immunotherapy; Knowledge; Laboratories; Machine Learning; Measurement; Measures; Medical History; Modality; Modeling; Nature; Patients; Population; Production; Proteome; Reproducibility; Research; Series; Socioeconomic Factors; System; Technology; Visualization; Work; base; biobank; biological systems; cohort; flexibility; genome analysis; learning strategy; machine learning algorithm; metabolome; microbiome; multiple omics; open source; patient population; patient subsets; predict clinical outcome; programs; response

Machine Learning for Integrative Modeling of the Immune System in Clinical Settings In response to an immunological challenge, immune cells act in concert forming complex and dense networks. A deep understanding of these immune responses is often the first step in developing immune therapies and diagnostic tests. Multivariate modeling algorithms can simultaneously consider all measured aspects of the immune system but requires prohibitively larger cohort sizes as technological advancements increase the number of measurements (a.k.a., “Curse of Dimensionality”). To address this, we propose a series of studies to develop machine learning algorithms for comprehensive profiling of the immune system in clinical settings. Particularly, for analysis of the immune system at a single-cell-level, we will leverage the stochastic nature of clustering algorithms to produce a robust pipeline for prediction of clinical outcomes. Next, we introduce the immunological Elastic-Net (iEN) algorithm, which addresses both the curse of dimensionality and reproducibility by integrating prior immunological knowledge into the models. The cellular systems that govern immunity act through symbiotic interactions with multiple interconnected biological systems. The simultaneous interrogation of these systems with suitable technologies can reveal otherwise unrecognized crosstalk. In collaboration with several leading laboratories, we have produced multiomics datasets (including analysis the genome, proteome, microbiome, and metabolome) in synchronized groups of patients. Using these coordinated datasets, we will evaluate several algorithms for combining multiple biological modalities while accounting for the intrinsic characteristics of each assay, to reveal biological cross- talk across various systems and increase combined predictive power. Importantly, numerous population- level factors (including medical history, environmental, and socioeconomic factors) significantly impact the immune system and studies focused on homogenous patient populations often lack generalizability to other populations. To address this, we will develop machine learning strategies to integrate population-level factors directly into our immunological data. These models will objectively define subpopulations of patients and enable flexibility in the coefficients of the models (and hence, the importance of the various biological measurements) in each group. This research program will be executed using data from several biorepositories focused on various diseases. This approach will ensure generalizability of our work to previously unseen datasets and increase the long-term impact of our findings. Throughout the proposal, a major area of focus is the development of visualization and model-reduction strategies that lay the foundation for interpretation of complex models. The machine learning algorithms developed will be readily applicable to a broad range of multiomics and multicohort studies and will be available as open-source software.

用于临床环境下免疫系统综合建模的机器学习 作为对免疫学挑战的回应，免疫细胞协同行动，形成复杂而密集的网络。 深入了解这些免疫反应通常是开发免疫疗法和 诊断性测试。多变量建模算法可以同时考虑所有测量的方面 免疫系统，但需要更大的队列大小，因为技术进步增加了 测量值的数量(也称为“维度诅咒”)。为了解决这个问题，我们提出了一系列研究，以 开发机器学习算法，用于在临床环境中对免疫系统进行全面分析。 特别是，对于单细胞级别的免疫系统分析，我们将利用 集群算法，以产生一个强大的管道，以预测临床结果。接下来，我们将介绍 免疫弹性网络(IEN)算法，同时解决了维度灾难和重复性问题 通过将先前的免疫学知识整合到模型中。 支配免疫的细胞系统通过与多个相互关联的 生物系统。用合适的技术同时审问这些系统可以揭示 否则会产生无法识别的串扰。在与几个领先的实验室合作下，我们生产了 同步的多组学数据集(包括分析基因组、蛋白质组、微生物组和代谢组) 一群群病人。使用这些协调的数据集，我们将评估几种组合多个 生物形态，同时说明了每一种化验的内在特征，以揭示生物交叉 跨各种系统进行对话，提高综合预测能力。重要的是，无数的人口- 水平因素(包括病史、环境和社会经济因素)显著影响 免疫系统和专注于同质患者群体的研究往往缺乏对其他 人口。为了解决这个问题，我们将开发机器学习策略来整合种群水平的因素 直接输入我们的免疫学数据。这些模型将客观地定义患者的亚群，并使 模型系数的灵活性(因此，各种生物测量的重要性) 在每组中。 这项研究计划将使用来自几个生物储存库的数据来执行，这些数据集中在 疾病。这种方法将确保我们的工作对以前未见过的数据集的概括性，并增加 我们发现的长期影响。在整个提案中，一个主要的重点领域是发展 可视化和模型简化战略，为解释复杂模型奠定了基础。这个 开发的机器学习算法将很容易适用于广泛的多组学和多队列 研究并将以开源软件的形式提供。