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: 10251069
• 负责人: Nima Aghaeepour
• 金额: $ 39.43万
• 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/10251069
• 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.

用于临床环境中免疫系统综合建模的机器学习 为了应对免疫学挑战，Immunocells在形成复杂和密集的网络的协同过程中起作用。 对这些免疫调查的深入了解通常是开发免疫和 诊断测试。多元建模算法可以简单地考虑所有测量方面 免疫系统，但随着技术进步的增加而需要更大的队列大小 测量次数（又称“维度的诅咒”）。为了解决这个问题，我们建议一系列研究 开发机器学习算法，用于在临床环境中进行全面分析免疫系统。 特别是，为了分析单细胞级别的免疫系统，我们将利用 聚类算法以产生强大的管道来预测临床结果。接下来，我们介绍 免疫弹性网（IEN）算法，它既应对维度和可重复性的诅咒 通过将先前的免疫学知识整合到模型中。 控制免疫学的细胞系统通过与多个互连的共生相互作用作用 生物系统。使用合适的技术对这些系统的简单询问可以揭示 否则未识别的串扰。与几个领先的实验室合作，我们已经生产了 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） 一组患者。使用这些协调的数据集，我们将评估多种算法以组合多个 在考虑每个测定的内在特征时，生物学方式，以揭示生物跨 在各种系统上进行交谈，并增加联合预测能力。重要的是，许多人口 - 水平因素（包括病史，环境和社会经济因素）显着影响 针对同质患者人群的免疫系统和研究通常缺乏对其他人的普遍性 人群。为了解决这个问题，我们将制定机器学习策略以整合人口级别的因素 直接进入我们的免疫数据。这些模型显然会定义患者的亚群，并启用 模型核心的灵活性（因此，各种生物测量的重要性） 在每个组中。 该研究计划将使用来自各种生物库的数据执行 疾病。这种方法将确保我们的工作能够将以前看不见的数据集的概括性扩大并增加 我们发现的长期影响。通过提案，重点的主要领域是发展 可视化和模型还原策略为复杂模型的解释奠定了基础。这 开发的机器学习算法将很容易适用于广泛的多组学和多霍普 研究并将作为开源软件提供。