MOMI Data Management

数据管理和分析核心：摘要 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反过来 提示免疫系统在整个怀孕期间从炎症转变为抗炎，转变为 平衡植入与同种异体胎儿的生长。相反，经济层面的调查已经开始指向一个 在整个怀孕期间出现的免疫时钟，可能推动这种胎儿保护之间的平衡 和母体免疫--然而，促成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，在这里我们的目标是 制定经济层面的数据--整合整个系统的措施，并使用疫苗作为机制 扰乱体内的系统。这些数据集将首次在怀孕期间捕获，从而构建 了解整个怀孕期间发生的免疫开关的基础数据以改善 孕产妇健康，制定治疗不孕不育的新战略，指导需要提高耐受性的疾病，如 以及改善新生儿健康。除了协助项目调查人员应用传统的 系统生物学数学工具，如差异表达、浓缩和聚类分析， 数据管理和分析核心(DMAC)将开发和使用一系列计算 源自工程和计算机科学领域的方法，包括机器学习 技巧。我们将重点介绍同时使用多个功能的建模框架 对反应的解释或预测，作为保护的多变量相关。此外，这些框架 可以研究这些多个变量是如何相互作用的，从而在生物学方面提供了潜在的进展 机制。将同时使用监督和非监督类别的算法，允许两种不同的 识别相关因素的视角。这一核心的努力将紧密地整合到每个 试验性项目。

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.