Quantitative Methods for HIV/AIDS Research

HIV/艾滋病研究的定量方法

• 批准号: 10216981
• 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/10216981
• 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; 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; Immersion; 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; Visualization; Work; Writing; base; collaboratory; complex data; computer science; computerized data processing; data complexity; design; education research; experience; experimental study; graduate student; insight; interdisciplinary collaboration; large datasets; 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.

艾滋病毒/艾滋病研究的定量方法 受到技术进步的刺激，艾滋病毒/艾滋病研究正在产生越来越大的复杂 数据集。例如，单个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响应以及几个流量或质量细胞术面板 表征细胞表型和抗原特异性结合，激活和功能。新基因组 诸如流体或液滴的单细胞RNA测序之类的技术表示数量级 数据复杂性的提高，并将将艾滋病毒/艾滋病研究进一步推向大数据领域。没有什么 在典型的艾滋病毒/艾滋病研究人员的培训中，研究人员为他或她准备了这样的数据分析。 鉴于数据的复杂性，不可避免的是，主要的艾滋病毒/艾滋病研究项目将不可避免地采用多种多样的 纪律团队科学方法。因此，艾滋病毒/艾滋病研究人员需要了解 数据科学和统计分析挑战与大型数据集有关，并且在统计中， 计算和数学研究人员了解艾滋病毒/艾滋病研究所面临的挑战。这 提案直接通过提供数据科学，统计思维和多种方式来解决这些需求 参数数据分析，专门针对Hivaids研究人员，以及身临其境的实习 领先的艾滋病毒/艾滋病研究实验室针对统计，数学，计算机科学和 工程。结合在一起，讲习班和实习将建立艾滋病毒/艾滋病的干部和定量 科学家能够有效地弥合纪律通信障碍，并形成有效和富有成效 合作伙伴关系。如果成功的话，该提案将作为培训下一步的模型 生成跨学科的艾滋病毒/艾滋病基础和转化科学研究人员。 该提案通过提供三个研讨会来满足技能发展要求的课程 每年（相当于每年36天）涵盖数据科学技能，统计思维和多种 参数数据分析对艾滋病毒/艾滋病研究人员的上下文化。前两个模块针对 艾滋病毒/艾滋病研究的研究生，博士后和新教师；第三个模块适合混合观众。 该提案通过为全职暑期实习提供了研究经验的要求 定量科学的研究生在领先的艾滋病毒/艾滋病实验室进行研究，共同 由艾滋病毒/艾滋病研究员和定量科学教师指导。我们建议有6个这样的 每年的项目，每个培训两个实习生。最后，该项目满足指导活动的要求， 非正式地通过在经验丰富的研究人员提供技术专长，建议和见解。 延长的研讨会和实习；并通过建立跨学科合作和运行正式 艾滋病毒/艾滋病生物医学研究人员的年度授予技巧，专门针对如何撰写 NIH赠款提交的数据分析计划。