Modeling Core

项目概要/摘要-建模核心 建模核心，作为CIMT的一部分，旨在将机器学习方法应用于临床和组学数据 由CRTT项目和核心产生，以开发重症肺炎模型并识别新的 生物标记和治疗靶点。使用迭代系统生物学方法，我们生成了详细的 发表在《自然》杂志上的一个模型，该模型描述了SARS-CoV-2肺炎的严重程度，与因肺炎引起的严重肺炎相比， 其他病原体，具有独特的宿主反应病理生物学，解释了其导致 长期重病。更重要的是，CIMT的模型预测了一种实验性药理学疗法的有效性。 SARS-CoV-2肺炎的干预-CRAC通道抑制剂Corp.在这次更新中，超级 （CRT 2）将继续利用生物材料（支气管肺泡灌洗液、鼻内 上皮、血液）与尖端的多组学技术和深度临床表型相结合， 肺炎发病机制的模型，可以增强临床决策。我们使用临床和组学 在本合同的第一个周期中收集和生成的数据，用于生成续约的初步数据。我们 在ICU中的离散时间和每天的相关生理测量，类似于医生如何 在重症监护室观察和治疗重症肺炎患者。我们的新方法克服了 将机器学习方法应用于临床数据，通常不考虑干预措施 它可以改变疾病的进程，通常只关注表现和最终的临床状态， 结果，类似于在两点之间画一条线。我们生成了一个低维的可解释的潜在 重症肺炎患者临床状态的空间模型我们发现，这些临床之间的过渡 SARS-CoV-2肺炎和其他类型肺炎患者的状态不同。通过预测结果 在这个临床潜在的空间上，我们建议鉴定与有利的和 不利的临床转变。我们将用这个重症肺炎的潜伏空间模型来检验这个假设 机器学习方法可以识别可解释的细胞和分子生物标志物， 重症肺炎临床过程中有利和不利的临床转变。我们将 在三个相互关联的具体目标中检验这一假设： 目的1：生成临床状态和转换（疾病）的可解释潜在空间模型 轨迹）中使用的数据收集的严重肺炎患者在2012年2月。 目的2：确定细胞和分子生物标志物以及预测转变的临床干预措施 严重肺炎患者的不利和有利临床状态之间的差异 收集到的数据。 目标3：将使用WITT 2生成的模型推广到外部数据集。

Project Summary/Abstract – Modeling Core The Modeling Core, as part of SCRIPT, aimed to apply machine learning approaches to clinical and -omics data generated by the SCRIPT projects and cores to develop a models of severe pneumonia and identify novel biomarkers and therapeutic targets. Using an iterative systems biology approach, we generated a detailed model, published in Nature, of how severe SARS-CoV-2 pneumonia, in contrast with severe pneumonia due to other pathogens, possesses a peculiar host response pathobiology that explains its propensity to cause prolonged critical illness. Importantly, SCRIPT’s model predicted the efficacy of an experimental pharmacologic intervention in SARS-CoV-2 pneumonia – the CRAC channel inhibitor Auxora. In this renewal, Super-SCRIPT (SCRIPT2) will continue to leverage serial sampling of biological materials (bronchoalveolar lavage fluid, nasal epithelium, blood) paired with cutting-edge multi-omics technologies and deep clinical phenotyping to develop models of pneumonia pathogenesis which could augment clinical decision making. We used clinical and -omics data collected and generated during the first cycle of this award to generate preliminary data for the renewal. We discretized time in the ICU and related physiological measures on a per-day basis, similar to how physicians view and treat patients with severe pneumonia in the ICU. Our novel approach overcomes a critical limitation in the application of machine learning approaches to clinical data, which often do not take into account interventions that can change the course of the disease and typically focus only on clinical state at presentation and ultimate outcome, analogous to drawing a line between two points. We generated a low-dimensional interpretable latent space model of clinical states in patients with severe pneumonia. We show that transitions between these clinical states are different in patients with SARS-CoV-2 pneumonia and other types of pneumonia. By projecting results of -omics assays onto this clinical latent space, we propose to identify biomarkers associated with favorable and unfavorable clinical transitions. We will use this latent space model of severe pneumonia to test the hypothesis that machine learning approaches can identify interpretable cellular and molecular biomarkers of favorable and unfavorable clinical transitions during the clinical course of severe pneumonia. We will test this hypothesis in three interrelated Specific Aims: Aim 1: To generate an interpretable latent space model of clinical states and transitions (disease trajectories) in patients with severe pneumonia using data collected within SCRIPT2. Aim 2: To identify cellular and molecular biomarkers and clinical interventions predictive of transitions between unfavorable and favorable clinical states in patients with severe pneumonia using data collected within SCRIPT2. Aim 3: To generalize models generated using SCRIPT2 to external datasets.