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Big Data Training for Translational Omics Research

转化组学研究的大数据培训

基本信息

批准号：
9297305
负责人：
MIN ZHANG
金额：
$ 15.76万
依托单位：
PURDUE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-30 至 2019-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9297305
关键词：
Address Administrator Archives Area Awareness Big Data Bioconductor Bioinformatics Biological Biology Biomedical Research Case Study Clinical Collaborations Collection Communities Competence Complement Computers Computing Methodologies Data Data Analyses Data Collection Data Science Education Educational Curriculum Educational Materials Explosion Foundations Genome Goals Health Home environment 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 clinical phenotype computer science computerized tools course implementation data resource density design epigenome experience graduate student improved interest knowledge translation metabolome prevent programs public health relevance repository response skills statistics tool transcriptome

项目摘要

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)的爆炸性增长捕获了多个级别的复杂性，有可能极大地加速知识从工作台到床边的转换。然而，这些数据的有效利用需要计算机科学、统计学和生物信息学方面的技能，以及详细的生物学和医学知识，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方面的培训。因此，许多生物医学科学家对他们无法：(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进行有效沟通感到沮丧。这些障碍严重阻碍了迄今积累的大量大数据的翻译应用。为了克服这些挑战，该团队提议创建一个基于案例研究的夏季培训课程，专门为大数据分析方面的新手生物医学研究人员设计。研究人员对中西部大学和十大大学的管理人员和研究人员进行了调查，确定了开设这门课程的必要性。本课程将提高有关生物医学大数据潜在用途的知识，并将培养定位、访问、管理、可视化、分析和集成各种公开可用的大数据类型的技能。拟议的大数据培训计划有三个目标：(1)在生物医学研究中介绍大数据的基本概念，以提高人们对这种研究方法的价值的认识；(2)提供面对面的教学，培养大数据科学所需的技术能力；(3)利用HUBZO平台开发教育和数据分析资源，以帮助我们的面对面教学，并提供加强和扩展技术技能的教学后机会。该课程将利用可用的大数据资源和工具，以便生物学家能够在短时间内高效地探索大数据。该教育项目将面向研究生、博士后实习生、内科科学家和生物医学科学家，他们拥有强大的生物医学背景，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。这门课程将集中在普渡大学，这是一所公认在统计学和计算机科学方面有优势的大型公立大学，目标是为中西部地区的科学家提供服务。此外，普渡大学开发的独特技术HUBZO平台将用于容纳计算工具和提供教育计划，并降低对参与者构成挑战的技术壁垒。这种方法将补充生物医学培训计划中的经典课程，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，并在研究团队内部进行交流。