Quantitative Methods for HIV/AIDS Research

HIV/艾滋病研究的定量方法

• 批准号: 9767663
• 负责人: Cliburn C Chan
• 金额: $ 30.71万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-21 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767663
• 关键词: AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Adopted; Adoption; Antibodies; Antigens; Appointment; Basic Science; Big Data; Binding; Bioconductor; Biological; Biological Assay; Biomedical Engineering; Case Study; Collaborations; Communication; Communication Barriers; Complex; Computers; Cytometry; Data; Data Analyses; Data Science; Data Set; Discipline; Docking; Educational Curriculum; Educational Materials; Educational process of instructing; Educational workshop; Engineering; Environment; Epitopes; Exposure to; Faculty; Foundations; Funding; Future; Genomics; Grant; HIV; Imagery; Immersion Investigative Technique; Institution; Internships; Laboratories; Laboratory Research; Laboratory Scientists; Learning Skill; Mathematics; Mentors; Methods; Modeling; Phenotype; Postdoctoral Fellow; Research; Research Personnel; Research Project Grants; Resources; Running; Science; Scientist; Series; Statistical Data Interpretation; Technical Expertise; Technology; Thinking; Time; Training; Translational Research; United States National Institutes of Health; Universities; Viral; Work; Writing; base; collaboratory; computer science; computerized data processing; design; education research; experience; experimental study; graduate student; insight; interdisciplinary collaboration; member; multidisciplinary; next generation; programs; response; single-cell RNA sequencing; skill acquisition; skills; statistics; summer internship; vaccine trial

Quantitative Methods for HIV/AIDS Research Spurred by recent technological advances, HIV/AIDS research is generating increasingly large and complex data sets. For example, the data collected in a single vaccine trial might include viral sequence, antibody binding and functional responses to multiple epitopes, and several flow or mass cytometry panels characterizing cellular phenotype and antigen-specific binding, activation and function. New genomic technologies such as Fluidigm or Droplet based single-cell RNA sequencing represent orders of magnitude increases in data complexity, and will push HIV/AIDS research even further into the realm of big data. Nothing in the training of the typical HIV/AIDS researcher prepares him or her for data analysis on such a scale. Given the data complexity, it is inevitable that major HIV/AIDS research projects will evolve to adopt a multi- disciplinary team science approach. Therefore, there is a need for HIV/AIDS researchers to understand the data science and statistical analysis challenges associated with large data sets, and for statistical, computational and mathematical researchers to understand the challenges specific to HIV/AIDS research. This proposal directly addresses these needs by providing workshops in data science, statistical thinking and multi- parameter data analysis, catered specifically to HIVAIDS researchers, as well as immersive internships in leading HIV/AIDS research laboratories for graduate students in statistics, mathematics, computer science and engineering. Combined, the workshops and internships will build a cadre of HIV/AIDS and quantitative scientists able to effectively bridge disciplinary communication barriers, and form effective and productive collaborative partnerships. If successful, this proposal will serve as a model for the training of the next- generation of inter-disciplinary HIV/AIDS basic and translational science researchers. The proposal fulfills the Courses for Skills Development requirement by offering three modules of workshops every year (equivalent to 36 full days per year) covering data science skills, statistical thinking and multi- parameter data analysis contextualized for HIV/AIDS researchers. The first two modules are targeted at graduate students, postdocs and new faculty in HIV/AIDS research; the third module is for a mixed audience. The proposal fulfills the Research Experiences requirement by providing full-time summer internships for graduate students in the quantitative sciences to conduct research in leading HIV/AIDS laboratories, co- mentored by an HIV/AIDS researcher and quantitative science faculty member. We propose to have 6 such projects each year, each training two interns. Finally, the project fulfills the Mentoring Activities requirement, informally by providing technical expertise, advice and insight from experienced researchers during the extended workshops and internships; and formally by establishing interdisciplinary collaborations and running an annual grantsmanship session for HIV/AIDS biomedical researchers specifically focused on how to write the data analysis plan for an NIH grant submission.

艾滋病研究的定量方法 在最近技术进步的推动下，艾滋病毒/艾滋病研究正在产生越来越大和复杂的 数据集。例如，在一次疫苗试验中收集的数据可能包括病毒序列、抗体 对多个表位的结合和功能反应，以及几个流式或质谱细胞术组 表征细胞表型和抗原特异性结合、活化和功能。新的基因组 基于Fluidigm或Droplet的单细胞RNA测序等技术代表了 数据复杂性的增加，将推动艾滋病毒/艾滋病研究进一步进入大数据领域。没有什么 在对典型的艾滋病研究人员的培训中，他或她为这种规模的数据分析做好了准备。 鉴于数据的复杂性，主要的艾滋病毒/艾滋病研究项目将不可避免地采取多方面的方法， 学科团队科学方法。因此，艾滋病毒/艾滋病研究人员需要了解 与大型数据集相关的数据科学和统计分析挑战，以及统计， 计算和数学研究人员了解艾滋病毒/艾滋病研究的具体挑战。这 提案通过提供数据科学、统计思维和多学科研讨会直接满足了这些需求， 参数数据分析，专门为艾滋病毒艾滋病研究人员，以及沉浸式实习， 领先的艾滋病毒/艾滋病研究实验室的研究生在统计，数学，计算机科学和 工程.讲习班和实习将结合起来，建立一个艾滋病毒/艾滋病和量化的干部队伍， 科学家能够有效地弥合学科沟通障碍，并形成有效和富有成效的 合作伙伴关系。如果成功的话，这一建议将成为培训下一个- 培养跨学科的艾滋病毒/艾滋病基础和转化科学研究人员。 该提案通过提供三个讲习班模块来满足技能发展课程的要求 每年（相当于每年36天），涵盖数据科学技能，统计思维和多 参数数据分析的背景下，艾滋病毒/艾滋病研究人员。前两个模块的目标是 研究生、博士后和艾滋病毒/艾滋病研究的新教员;第三个模块面向混合受众。 该提案通过为以下人员提供全职暑期实习机会来满足研究经验的要求： 研究生在定量科学进行研究，在领先的艾滋病毒/艾滋病实验室，合作， 由艾滋病毒/艾滋病研究人员和定量科学教员指导。我们建议有6个这样的 每年培训两名实习生。最后，项目满足了指导活动的要求， 非正式地提供技术专长，咨询意见和有经验的研究人员的见解， 扩大讲习班和实习;并正式通过建立跨学科合作和运行 艾滋病毒/艾滋病生物医学研究人员的年度研讨会特别关注如何编写 数据分析计划，用于NIH拨款申请。