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相反 表明免疫系统在整个怀孕期间从炎症转变为抗炎， 平衡移植胎儿的植入和生长。相反，OMIC级别的调查已经开始指向一个 免疫时钟出现在整个怀孕期间，可能会推动胎儿保护之间的平衡 和母体免疫-然而，有助于这种生物学的具体机制，以及是否相同 变化同时发生在整个免疫系统是不完全理解。因此，我们的目标是 制定OMIC级数据-整合整个系统的措施，并利用疫苗作为一种机制， 干扰体内系统。这些数据集将首次在整个妊娠期被捕获， 基础数据，以了解整个怀孕期间发生的免疫开关，以改善 产妇保健，制定治疗不孕症的新战略，指导需要提高耐受性的疾病， 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，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 从工程和计算机科学领域产生的方法，包括机器学习 技术.我们将强调建模框架，其中多个功能同时用于 解释或预测响应，作为保护的多变量相关因素。此外，这些框架 可以研究这些多个变量如何相互作用，提供生物学见解的潜在进展， 机制监督和非监督类的算法都将被利用，允许两种不同的 识别相关因素的观点。该核心的努力将密切融入每一个 实验项目。

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.