Administrative Supplement to: Big Data Training for Translational Omics Research

行政补充：转化组学研究大数据培训

• 批准号: 9243817
• 负责人: MIN ZHANG
• 金额: $ 14.42万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9243817
• 关键词: Address; Administrative Supplement; Administrator; Archives; Area; Awareness; Big Data; Bioconductor; Bioinformatics; Biological; Biology; Biomedical Research; Case Study; Clinical; Collaborations; Collection; Communities; Complement; Computers; Computing Methodologies; Data; Data Analyses; Data Collection; Data Science; Education; Educational Curriculum; Educational Materials; Explosion; Foundations; Genome; Goals; Health; Home environment; Housing; Human; Image; Institution; Instruction; Knowledge; Medical; Medicine; Midwestern United States; Participant; Physicians; Population; Positioning Attribute; Proteome; Records; Research; Research Personnel; Resources; Schools; Science; Scientist; Source; Statistical Methods; Surveys; Technical Expertise; Technology; The Cancer Genome Atlas; Time; Training; Training Programs; Universities; base; bench to bedside; big biomedical data; biomedical scientist; computer science; computerized tools; course implementation; density; design; epigenome; experience; graduate student; improved; interest; knowledge translation; metabolome; prevent; programs; repository; response; skills; statistics; tool; transcriptome

 DESCRIPTION (provided by applicant): The explosion of biomedical big data (e.g. imaging, clinical records, and "omic" analyzes) that captures multiple levels of complexity has the potential to dramatically accelerate the translation of knowledge from bench to bedside. However, the effective use of these data requires skills in computer science, statistics, and bioinformatics, as well as detailed knowledge of biology and medicine to aid in the interpretation of the data analysis. Unfortunately, biomedical researchers are not trained in the computational and statistical methods needed to handle high-density biomedical big data. As a result, many biomedical scientists are frustrated by their inability to: (a) analyze big data, (b) utilize the valuable public resources containing big data, and (c) effectively communicate with computer scientists, statisticians and bioinformaticians. These barriers have significantly hampered the translational application of the large body of big data that has accumulated thus far. In order to overcome these challenges, this team proposes to create a summer training course that is built upon case studies and that is specifically designed for biomedical researchers who are novices in big data analysis. The investigators identified the need for this course in a survey of administrators and researchers at Midwest and Big Ten universities. This course will raise knowledge of the potential uses of biomedical big data and will develop skills for locating, accessing, managing, visualizing, analyzing, and integrating various types of big data that are publicly available. The proposed big data training program has three goals: (1) introduce the fundamental concepts of big data in biomedical research to raise awareness of the value of this research approach, (2) provide face-to-face instruction that develops the technical competency needed for big data science, and (3) develop educational and data analysis resources using the HUBzero platform to aid our face-to-face instruction and provide post-instruction opportunities for reinforcing and expanding technical skills. The course will exploit available big data resources and tools so that biologists can productively explore big data within a short time. The educational program will target graduate students, postdoctoral trainees, physician-scientists and biomedical scientists, with strong biomedical backgrounds but who have limited advanced coursework in statistics, bioinformatics, and computer science. This course will be centered at Purdue University, a large public university with recognized strengths in statistics and computer science, with a goal to serve scientists in the Midwest area. Also, the HUBzero platform, a unique technology developed at Purdue, will be used to house computational tools and deliver the educational program, and to lower the technical barriers that challenge participants. This approach will complement the classical curricula in biomedical training programs and serve as a foundation for more advanced training. The proposed course is directly responsive to RFA-HG-14-008 because it will enable biomedical researchers to more confidently explore existing biomedical big data, implement their own data collection and analysis plans, and communicate within research teams.

 描述（由申请人提供）：生物医学大数据（例如成像、临床记录和“组学”分析）的爆炸式增长，捕获了多个复杂程度，有可能大大加速知识从实验室到临床的转化。然而，这些数据的有效使用需要计算机科学，统计学和生物信息学方面的技能，以及生物学和医学的详细知识，以帮助解释数据分析。不幸的是，生物医学研究人员没有接受过处理高密度生物医学大数据所需的计算和统计方法的培训。因此，许多生物医学科学家因无法：（a）分析大数据，（B）利用包含大数据的宝贵公共资源，以及（c）与计算机科学家、统计学家和生物信息学家有效沟通而感到沮丧。这些障碍严重阻碍了迄今为止积累的大量大数据的转化应用。为了克服这些挑战，该团队建议创建一个夏季培训课程，该课程以案例研究为基础，专为大数据分析新手的生物医学研究人员设计。调查人员在对中西部和十大大学的管理人员和研究人员的调查中确定了对这门课程的需求。本课程将提高生物医学大数据的潜在用途的知识，并将开发定位，访问，管理，可视化，分析和整合各种类型的大数据是公开的技能。拟议的大数据培训计划有三个目标：（1）介绍生物医学研究中大数据的基本概念，以提高对这种研究方法价值的认识，（2）提供面对面的指导，培养大数据科学所需的技术能力，以及（3）使用HUBzero平台开发教育和数据分析资源，以帮助我们进行面对面的指导，并提供事后加强和扩大技术技能的指导机会。该课程将利用现有的大数据资源和工具，使生物学家可以在短时间内有效地探索大数据。该教育计划将针对研究生，博士后培训生，医生科学家和生物医学科学家，具有较强的生物医学背景，但在统计学，生物信息学和计算机科学方面的高级课程有限。本课程将以普渡大学为中心，普渡大学是一所大型公立大学，在统计和计算机科学方面具有公认的优势，目标是为中西部地区的科学家服务。此外，HUBzero平台是普渡大学开发的一项独特技术，将用于容纳计算工具和提供教育计划，并降低挑战参与者的技术障碍。这种方法将补充生物医学培训项目中的经典课程，并作为更高级培训的基础。拟议的课程直接响应RFA-HG-14-008，因为它将使生物医学研究人员能够更自信地探索现有的生物医学大数据，实施自己的数据收集和分析计划，并在研究团队中进行交流。