MOMI Data Management

MOMI数据管理

• 批准号: 10420111
• 负责人: DOUGLAS A LAUFFENBURGER
• 金额: $ 47.08万
• 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/10420111
• 关键词: 2019-nCoV; Algorithms; Allografting; Anti-Inflammatory Agents; Atlases; Biological; Biology; COVID-19 pandemic; Categories; Chronology; Clinical; Cluster Analysis; Communicable Diseases; Complex; Computer Analysis; Computer Models; Consultations; Custom; Data; Data Analyses; Data Set; Development; Discipline; Disease; Engineering; Equilibrium; Etiology; Fetal Growth; Foundations; Global Awareness; Goals; 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; Supervision; System; Systems Biology; Techniques; Time; Vaccination; Vaccines; Woman; Work; base; cell type; computer framework; computer science; 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; therapeutic vaccine; 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：摘要 虽然先前被认为是免疫抑制的状态，但新兴的免疫学研究是 表明免疫系统在整个怀孕期间从炎症到抗炎，转变为抗炎 平衡胎儿同种异体移植的植入和生长。相反，OMIC的调查已经开始指出 在整个怀孕期间出现的免疫时钟可能会在胎儿保护之间取得这种平衡 和孕产妇的免疫力 - 然而，有助于该生物学的特定机制以及是否相同 在整个免疫系统中仅发生变化。那我们的目标是 制定OMIC级别数据 - 整合整个系统的措施，并使用疫苗作为机制 扰动系统在体内。这些数据集将首次在妊娠之间捕获 基础数据以了解整个怀孕过程中发生的免疫学开关 孕产妇的健康，制定新的治疗不育的策略，指导需要提高容忍度的疾病，作为 改善新生儿健康。除了协助项目调查人员使用传统 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）将开发并采用一系列计算 工程和计算机科学领域引起的方法，包括机器学习 技术。我们将强调建模框架，其中多个功能同时使用 响应的解释或预测，作为保护的多变量相关。而且，这些框架 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步 机制。将利用受监督和无监督的算法类别，允许两种不同的 识别相关性的观点。该核心的努力将密切整合到每个 实验项目。