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.

临床环境中免疫系统综合建模的机器学习 为了应对免疫挑战，免疫细胞协同作用，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的第一步 诊断测试。多变量建模算法可以同时考虑所有测量的方面。 免疫系统，但随着技术进步增加，需要更大的队列规模 为了解决这个问题，我们提出了一系列研究来解决这个问题。 开发机器学习算法，用于在临床环境中全面分析免疫系统。 特别是，对于单细胞水平的免疫系统分析，我们将利用 接下来，我们介绍了用于预测临床结果的强大管道的聚类算法。 免疫学 Elastic-Net (iEN) 算法，解决了维度和再现性的灾难 通过将先前的免疫学知识整合到模型中。 控制免疫的细胞系统通过与多个相互关联的共生相互作用来发挥作用。 使用适当的技术同时询问这些系统可以揭示一些信息。 我们与几个领先的实验室合作，生产了其他无法识别的串扰。 同步多组学数据集（包括分析基因组、蛋白质组、微生物组和代谢组） 使用这些协调的数据集，我们将评估用于组合多个患者的几种算法。 生物模式，同时考虑每种测定的内在特征，以揭示生物交叉 重要的是，跨不同系统进行交谈并结合起来可以提高预测能力。 水平因素（包括病史、环境和社会经济因素）显着影响 免疫系统和针对同质患者群体的研究往往缺乏对其他人群的普遍适用性 为了解决这个问题，我们将开发机器学习策略来整合人口层面的因素。 这些模型将客观地定义患者的亚群并使其成为可能。 模型系数的灵活性（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多个生物样本库的数据来执行，这些生物样本库专注于各种 这种方法将确保我们的工作可推广到以前未见过的数据集，并增加 我们的研究结果的长期影响在整个提案中，一个主要关注领域是发展。 可视化和模型简化策略为复杂模型的解释奠定了基础。 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 研究并将作为开源软件提供。