MOMI Data Management

MOMI数据管理

• 批准号: 10611532
• 负责人: DOUGLAS A LAUFFENBURGER
• 金额: $ 14.58万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-19 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611532
• 关键词: 2019-nCoV; Algorithms; Allografting; Anti-Inflammatory Agents; Atlases; Biological; Biology; COVID-19 pandemic; Categories; Chronology; Clinical; Cluster Analysis; Communicable Diseases; Computer Analysis; Computer Models; Custom; Data; Data Analyses; Data Set; Development; Discipline; Disease; Engineering; Equilibrium; Etiology; Global Awareness; Goals; Growth; Immune; Immune response; Immune system; Immunity; Immunologics; Immunology; Immunosuppression; Individual; Infection; Infertility; Inflammatory; Intervention; Intuition; Investigation; Lactation; Machine Learning; Maternal Health; Maternal-fetal medicine; Maternally-Acquired Immunity; Mathematical Biology; Mathematics; Measurement; Measures; Methods; Modeling; Nature; Newborn Infant; Organ; Pregnancy; Pregnancy Trimesters; Process; Property; Publications; Recording of previous events; Research Personnel; Sampling; Shapes; Statistical Data Interpretation; System; Systems Biology; Techniques; Time; Vaccination; Vaccines; Woman; Work; cell type; complex data; computer framework; computer science; data integration; data management; differential expression; diverse data; fetal; heterogenous data; implantation; improved; in vivo; insight; maternal vaccination; multidisciplinary; multiple omics; neonatal health; novel; novel strategies; novel therapeutics; novel vaccines; operation; pathogen; predicting response; pregnant; programs; prophylactic; tool; vaccine platform; vaccine-induced immunity; vaccinology

Data Management and Analysis Core: Summary While previously regarded as a state of immunosuppression, emerging immunological studies conversely suggest that immune system shifts throughout pregnancy from inflammatory to anti-inflammatory, shifting to balance implantation and growth of the fetal allograft. Instead, OMIC level investigation has begun to point to an immunological clock that appears throughout pregnancy that may drive this balance between fetal-protection and maternal immunity- however the specific mechanisms that contribute to this biology and whether the same changes occur simultaneously throughout the immune system is incompletely understood. Thus, here we aim to develop an OMIC level data – integrating measures across the system and using vaccines as a mechanism to perturb the system in vivo. These datasets will be captured across gestation for the first time, building the foundational data to understand the immunological switches that occur throughout pregnancy to improve maternal health, develop novel strategies to treat infertility, to guide diseases requiring improved tolerance, as well as to improve neonatal health. In addition to assisting Project investigators with application of traditional systems biology mathematical tools, such as differential expression, enrichment, and clustering analysis, the Data Management and Analysis Core (DMAC) will develop and employ a spectrum of computational approaches arising from the realms of engineering and computer science, including machine learning techniques. We will emphasize modeling frameworks in which multiple features are used concomitantly for explanation or prediction of responses, as multi-variate correlates of protection. Moreover, these frameworks can examine how these multiple variables interact, offering potential advances in biological insights concerning mechanism. Both supervised and unsupervised classes of algorithms will be utilized, permitting two different perspectives on identifying correlates. The efforts of this Core will be intimately integrated into each of the experimental Projects.

数据管理和分析核心：总结 虽然以前被认为是一种免疫抑制状态，但新兴的免疫学研究却相反 表明免疫系统在整个怀孕期间从炎症转变为抗炎，转变为 相反，OMIC 水平的调查已开始指出胎儿同种异体移植物的平衡植入和生长。 整个怀孕期间出现的免疫时钟可能会推动胎儿保护之间的平衡 和母体免疫力 - 然而，有助于这种生物学的具体机制以及是否相同 整个免疫系统同时发生的变化尚不完全清楚，因此，我们的目标是。 开发 OMIC 级别的数据——整合整个系统的措施并使用疫苗作为机制 这些数据集将首次在妊娠期间捕获，从而构建 了解整个怀孕期间发生的免疫转换的基础数据 孕产妇健康，制定治疗不孕症的新策略，指导需要提高耐受性的疾病，例如 除了协助项目研究人员应用传统方法外，还可以改善新生儿健康。 系统生物学数学工具，例如差异表达、富集和聚类分析， 数据管理和分析核心（DMAC）将开发和采用一系列计算 来自工程和计算机科学领域的方法，包括机器学习 我们将强调同时使用多个特征的建模框架。 对反应的解释或预测，作为保护的多变量相关性。 可以检查这些多个变量如何相互作用，从而提供有关生物学见解的潜在进展 将使用监督和非监督类别的算法，允许两种不同的算法。 确定相关性的观点将被紧密地融入到每个核心的工作中。 实验项目。