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

用于临床环境中免疫系统综合建模的机器学习 为了应对免疫挑战，免疫细胞一致行动，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的第一步， 诊断测试多变量建模算法可以同时考虑环境的所有测量方面。 但随着技术进步， 测量次数（也称为，”（《明史》）。为了解决这个问题，我们提出了一系列研究， 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 聚类算法，以产生用于预测临床结果的鲁棒管道。接下来，我们介绍 免疫弹性网络（iEN）算法，解决了维数灾难和再现性 通过将先前的免疫学知识整合到模型中。 控制免疫力的细胞系统通过与多个相互连接的 生物系统。用适当的技术同时询问这些系统可以揭示 否则无法识别的串扰。通过与多家领先的实验室合作，我们生产了 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） 患者群体。使用这些协调的数据集，我们将评估几种算法， 生物学模式，同时考虑每个测定的内在特征，以揭示生物学交叉， 在不同的系统之间进行对话，并提高综合预测能力。重要的是，许多人- 水平因素（包括病史、环境和社会经济因素）显著影响 免疫系统和研究集中在同质患者人群往往缺乏普遍性，以其他 人口。为了解决这个问题，我们将开发机器学习策略，以整合人口水平的因素 直接输入到我们的免疫学数据中这些模型将客观地定义患者亚群， 模型系数的灵活性（因此，各种生物测量的重要性） 在每一组中。 这项研究计划将使用来自几个生物储存库的数据执行，这些数据集中在各种 疾病这种方法将确保我们的工作对以前看不见的数据集的普遍性，并增加 我们的研究结果的长期影响。在整个提案中，一个主要的重点领域是发展 可视化和模型简化策略为复杂模型的解释奠定了基础。的 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。

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.