CORE B: Computational Biology and Statistical Modeling

核心 B：计算生物学和统计建模

• 批准号: 10458126
• 负责人: Leah Katzelnick
• 金额: $ 15.76万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-29 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10458126
• 关键词: Address; Affect; Age; Antibodies; Antibody Response; Antigens; B-Lymphocytes; California; Case-Control Studies; Cellular Immunity; Characteristics; Child; Childhood; Clinical; Cohort Studies; Collaborations; Computational Biology; Data; Databases; Dengue; Dengue Infection; Dengue Vaccine; Dengue Virus; Dengvaxia; Disease; Ensure; Epidemiology; Evolution; Experimental Designs; Geography; Goals; Helper-Inducer T-Lymphocyte; Humoral Immunities; Immune; Immunity; Immunologic Markers; Immunologics; Incidence; Individual; Infection; Linear Models; Link; Longevity; Maintenance; Maps; Measures; Memory B-Lymphocyte; Methods; Modeling; Nicaragua; Outcome; Pathogenesis; Pathogenicity; Pediatric cohort; Phylogenetic Analysis; Prevalence; Recording of previous events; Research; Sampling; Serology; Serotyping; Serum; Services; Severities; Severity of illness; Site; Statistical Data Interpretation; Statistical Models; Study of serum; System; T-Lymphocyte; Testing; Universities; Vaccinated; Vaccination; Vaccinee; Vaccines; Virus Diseases; Work; ZIKA; ZIKV infection; cohort; cross reactivity; design; epidemiology study; experience; flexibility; high dimensionality; improved; machine learning method; machine learning model; multidimensional data; neutralizing antibody; predictive marker; programs; random forest; seropositive; severe dengue; statistical and machine learning; support vector machine; vaccine-induced immunity; viral transmission

Computational Biology and Statistical Modeling Core (CORE B) (University of California, Berkeley) SUMMARY The Computational Biology and Statistical Modeling Core (Core B) will provide essential services to individual Projects and the Program Project (P01) as a whole by providing statistical support at each stage of research. Critically, Core B will enable the Projects to address key themes of the P01 by applying unifying computational biology, statistical, and machine learning approaches to study natural and vaccine-induced dengue humoral and cellular immunity. In Aim 1, Core B will conduct epidemiological analyses of the natural dengue virus (DENV) infection and dengue vaccine cohorts to inform the immunological studies proposed by Projects 1, 2, and 3. For the Nicaragua Pediatric Dengue Cohort Study, we will work closely with Core C to investigate dengue incidence before and after the introduction of Zika as well as how changing DENV transmission intensity affects dengue disease severity. For the Cebu Dengvaxia® cohort, we will estimate DENV infection and dengue disease incidence stratified by baseline DENV serostatus and vaccination history to support the immune correlates studies proposed by Project 2. We will also compare these two pediatric cohorts to understand how geography, DENV transmission intensity, ZIKV infection history, and serotype prevalence affect dengue disease. Phylogenetic and phylodynamic analyses will be conducted for all DENV isolates from both cohorts. In Aim 2, we will support each Project individually and conduct cross-Project analyses to identify immune markers that correlate with protection against symptomatic dengue and pathogenesis of severe dengue disease. This aim encompasses immune correlates of natural and vaccine-induced DENV immunity. We will work with each Project to design case-control studies to test how DENV-specific serum antibody, B cell, and T cell characteristics predict distinct clinical outcomes. Core B will analyze the multi-dimensional datasets produced by the Projects to classify clinical outcomes using straightforward machine learning methods such as generalized linear models, flexible approaches such as random forests, and methods that are robust to outliers such as support vector machines, all with regularization to reduce model complexity. In Aim 3, we will support the Projects in studying children who have experienced natural primary and secondary DENV infections to identify immune markers that predict maintenance anti-DENV immunity. We will use regression models to determine how antibody and helper T cell characteristics measured soon after infection predict both the magnitude and the durability of cross-reactive and type-specific antibody responses. We will then incorporate the predictive immune markers into linear and more flexible mixed-effects regression models to fit antibody dynamics following primary and secondary DENV infection. Parallel analyses will be conducted for baseline seronegative and seropositive vaccine recipients, enabling direct comparison of the determinants of immune longevity following natural DENV infection and vaccination. We also compare the systems serology measures performed on post-primary, pre-secondary, and post-secondary natural DENV infection samples in the same individuals to test for changes in antibody antigen recognition and Fc effector characteristics. Collectively, these three Aims are critical to the research proposed in the Projects and will work toward the overarching P01 goal to identify predictive and mechanistic anti-DENV immune characteristics that provide long-term protection against dengue disease.!

计算生物学和统计建模核心（CORE B） （加州大学伯克利分校） 总结 计算生物学和统计建模核心（核心B）将提供基本服务， 通过在每个阶段提供统计支持， research.重要的是，核心B将使项目能够通过应用统一的 计算生物学，统计学和机器学习方法来研究自然和疫苗诱导 登革热的体液和细胞免疫。在目标1中，核心B将对自然感染者进行流行病学分析， 登革病毒（DENV）感染和登革疫苗队列，为免疫学研究提供信息， 项目1、2和3。对于尼加拉瓜儿童登革热队列研究，我们将与核心C密切合作， 调查寨卡病毒引入前后的登革热发病率以及DENV的变化 传播强度影响登革热疾病的严重程度。对于宿务Dengvaxia®队列，我们将估计DENV 根据基线DENV血清状态和疫苗接种史分层的感染和登革热疾病发病率，以支持 项目2提出的免疫相关性研究。我们还将比较这两个儿科队列， 了解地理、DENV传播强度、ZIKV感染史和血清型流行率如何影响 登革热将对来自两个组织的所有DENV分离株进行系统发育和病毒动态分析。 同伙在目标2中，我们将单独支持每个项目，并进行跨项目分析，以确定 与预防症状性登革热和重症登革热发病机制相关免疫标志物 疾病该目的包括天然和疫苗诱导的DENV免疫的免疫相关性。我们将 与每个项目一起设计病例对照研究，以测试DENV特异性血清抗体、B细胞和T细胞如何影响DENV的免疫应答。 细胞特征预测不同的临床结果。核心B将分析多维数据集 该项目使用简单的机器学习方法对临床结果进行分类，例如 广义线性模型，灵活的方法，如随机森林，以及对离群值鲁棒的方法 例如支持向量机，所有这些都具有正则化以降低模型复杂性。在目标3中，我们将支持 研究经历过自然原发性和继发性DENV感染的儿童的项目， 鉴定预测维持抗DENV免疫的免疫标记物。我们将使用回归模型来 确定感染后不久测量的抗体和辅助T细胞特征如何预测 交叉反应性和类型特异性抗体应答的强度和持久性。然后我们将合并 将预测性免疫标志物转化为线性和更灵活的混合效应回归模型以拟合抗体 在原发性和继发性DENV感染后的动态。将对基线进行平行分析 血清阴性和血清阳性疫苗接种者，能够直接比较免疫反应的决定因素， 在自然DENV感染和疫苗接种后的寿命。我们还比较了系统血清学措施 在相同的环境中对原发后、继发前和继发后天然DENV感染样本进行了研究。 个体以测试抗体抗原识别和Fc效应子特征的变化。总的来说，这些 三个目标对于项目中提出的研究是至关重要的，并将朝着P01的总体目标努力， 鉴定提供针对以下疾病长期保护的预测性和机制性抗DENV免疫特征： 登革热！