Transforming Analytical Learning in the Era of Big Data

大数据时代的分析学习变革

• 批准号: 9888408
• 负责人: Jian Kang
• 金额: $ 25.1万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9888408
• 关键词: Admission activity; Adverse drug effect; Advocate; Alzheimer' s Disease; Area; Arts; Award; Basic Science; Big Data; Big Data to Knowledge; Bioinformatics; Biological Markers; Biological Sciences; Biology; Biometry; Cardiovascular Diseases; Cardiovascular system; Case Study; Clinical; Clinical Sciences; Code; Collaborations; Collection; Computing Methodologies; Data; Data Science; Data Scientist; Data Set; Development; Diabetes Mellitus; Disease; Educational process of instructing; Educational workshop; Electronic Health Record; Engineering; Epidemiology; Epigenetic Process; Evaluation; Exposure to; Faculty; Female; Foundations; Funding; Funding Mechanisms; Generations; Genetic; Genomics; Goals; Grant; Health; Health Sciences; Human; Image; Influentials; Information Sciences; Injury; International; Kidney Diseases; Learning; Literature; Macular degeneration; Malignant Neoplasms; Medical; Medical Imaging; Medical Records; Medicine; Mentors; Methodology; Methods; Michigan; Minority Groups; Natural Language Processing; Neurosciences; Obesity; Oral; Participant; Prevention; Privatization; Problem Sets; Public Health Informatics; Public Health Schools; Research; Research Personnel; Research Project Grants; Resources; STEM program; Schools; Science; Science, Technology, Engineering and Mathematics; Scientist; Social Behavior; Social Sciences; Statistical Methods; Statistical Models; Structure; Student recruitment; Students; Talents; Techniques; Traction; Training; Underrepresented Minority; United States National Institutes of Health; Universities; Woman; Work; base; big-data science; burden of illness; cluster computing; college; computer science; data integration; data management; data mining; data visualization; design; experience; graduate school preparation; graduate student; heterogenous data; high dimensionality; instructor; interest; lectures; medical schools; member; metabolomics; nervous system disorder; network models; next generation; novel therapeutics; open source; personalized medicine; population health; posters; precision medicine; programs; recruit; signal processing; skills; statistical and machine learning; statistics; student training; success; summer institute; summer program; summer research; symposium; tool; undergraduate student; underrepresented minority student; wiki

PROJECT SUMMARY In this dawning era of `Big Data' it is vital to recruit and train the next generation of biomedical data scientists in `Big Data'. The collection of `Big Data' in the biomedical sciences is growing rapidly and has the potential to solve many of today's pressing medical needs including personalized medicine, eradication of disease, and curing cancer. Realizing the benefits of Big Data will require a new generation of leaders in (bio)statistical and computational methods who will be able to develop the approaches and tools necessary to unlock the information contained in large heterogeneous datasets. There is a great need for scientists trained in this specialized, highly heterogeneous, and interdisciplinary new field of health big data. Thus, the recruitment of talented undergraduates in science, technology, engineering and mathematics (STEM) programs is vital to our ability to tap into the potential that `Big Data' offers and the challenges that it presents. The University of Michigan Undergraduate Summer Institute: Transforming Analytical Learning in the Era of Big Data will primarily draw from the expertise and experience of faculty from three different departments within three different schools at the University of Michigan: Biostatistics in the School of Public Health, Computer Science in the School of Engineering, Statistics in the College of Literature, Sciences and the Arts. The faculty instructors and mentors have backgrounds in Statistics, Computer Science, Information Science, Medicine, Population Health, Social and Biological Sciences. They have active research programs in a broad spectrum of methodological areas including statistical modeling, data mining, natural language processing, statistical and machine learning, large-scale optimization, matrix computation, medical computing, health informatics, high- dimensional statistics, distributed computing, missing data, causal inference, data management and integration, signal processing and medical imaging. The diseases and conditions they study include obesity, diabetes, cardiovascular disease, cancer, neurological disease, kidney disease, injury, macular degeneration and Alzheimer's disease. The areas of biology include neuroscience, genetics, genomics, metabolomics, epigenetics and socio-behavioral science. Undergraduate trainees selected will have strong quantitative skills and a background in STEM. The summer institute will consist of a combination of coursework, to raise the skills and interests of the participants to a sufficient level to consider pursuing graduate studies in `Big Data' science, along with an in depth mentoring component that will allow the participants to research a specific topic/project utilizing `Big Data'. We have witnessed tremendous enthusiasm and success with the current summer program on Big Data led by this team with 164 students trained in the last 4 years (2015-2018) including 90 female students and 30 students from underrepresented minority groups. Fourteen of these participants from the last three years are currently graduate students in Michigan Biostatistics. The ongoing program has gained traction in the national landscape of summer research programs with 20% rate of admission and 80% rate of acceptance among those who are offered this opportunity. The program has consistently received very strong evaluation and our past alumni have become brand ambassadors and advocates for our program. We plan to build on the success and legacy of this program in the next three year funding cycle of this grant (2019-2021). The overarching goal of our summer institute in big data is to recruit and train the next generation of big data scientists using a non-traditional, action-based learning paradigm. This six week long summer institute will recruit a group of approximately 45 undergraduates nationally and internationally, with 20 domestic students supported by the requested SIBS funding mechanism and others supported by supplementary institutional and foundation support. We propose to expose the trainees to diverse techniques, skills and problems in the field of health Big Data. They will be taught and mentored by a team of interdisciplinary faculty, reflecting the shared intellectual landscape needed for Big Data research. They will engage in mentored research projects in three primary areas of health big data: Electronic Health Records/Medical Claims, Genomics and Imaging. Some of the projects will be defined in the area of cardiovascular precision medicine, defined by a team of highly quantitative researchers engaged in cardiovascular research that uses big data. At the conclusion of the program there will be a concluding capstone symposium showcasing the research of the students via poster and oral presentation. There will be lectures by U-M researchers, outside guests and a professional development workshop to prepare the students for graduate school. We propose an inter-SIBS collaboration with Dordt College summer program trainees who will attend this concluding symposium. The resources developed for the summer institute, including lectures, assignments, projects, template codes and datasets will be freely available through a wiki page so that this format can be replicated anywhere in the world. This democratic dissemination plan will lead to access of teaching and training material for undergraduate students in this new field across the world. We will offer multiple professional development opportunities and resources for graduate school preparation to our trainees so that they can reflect and plan beyond their senior year. All of our proposed activities are reflected through our three specific aims: Teaching, Mentoring and Dissemination.

项目摘要 在这个“大数据”时代的时代，招募和培训下一代生物医学数据科学家至关重要 “大数据”。生物医学科学中“大数据”的收集正在迅速增长，并且有可能解决 当今许多紧迫的医疗需求，包括个性化医学，根除疾病和治愈 癌症。意识到大数据的好处将需要（BIO）统计和 将能够开发出解锁信息所需的方法和工具的计算方法 包含在大型异构数据集中。在这个专业的，高度的科学家中，非常需要科学家 异质和跨学科的新型健康大数据领域。因此，招募才华 科学，技术，工程和数学（STEM）计划的大学生对于我们的能力至关重要 利用“大数据”所提供的潜力及其提出的挑战。 密歇根大学本科夏季学院：在时代的转化分析学习 大数据主要来自三个不同部门的教师的专业知识和经验 密歇根大学的三所不同学校：公共卫生学院的生物统计学，计算机 工程学院的科学，文学学院的统计学，科学与艺术学院。教师 讲师和导师具有统计，计算机科学，信息科学，医学的背景， 人口健康，社会和生物科学。他们在各种各样的领域都有积极的研究计划 方法论领域，包括统计建模，数据挖掘，自然语言处理，统计和 机器学习，大规模优化，矩阵计算，医学计算，健康信息学，高级 维统计，分布式计算，数据缺失，因果推理，数据管理和集成， 信号处理和医学成像。他们研究的疾病和状况包括肥胖，糖尿病， 心血管疾病，癌症，神经疾病，肾脏疾病，损伤，黄斑变性和 阿尔茨海默氏病。生物学领域包括神经科学，遗传学，基因组学，代谢组学，表观遗传学 和社会行为科学。选定的本科学员将具有强大的定量技能和 茎的背景。夏季学院将包括课程的组合，提高技能和 参与者的兴趣达到了足够的水平，可以考虑从事“大数据”科学领域的研究生学习 借助深度指导组件，将允许参与者研究特定主题/项目利用 “大数据”。我们目睹了当前的夏季计划的巨大热情和成功 该团队领导的数据在过去4年（2015-2018）中接受了164名学生，其中包括90名女学生 来自代表性不足的少数群体的30名学生。最后三名参与者中有14位 目前是密歇根州生物统计学的研究生。正在进行的计划在 夏季研究计划的全国景观，入院率为20％和80％ 在那些提供这个机会的人中。该计划始终接受了非常强大的评估 我们过去的校友已成为我们计划的品牌大使和倡导者。我们计划以 该计划的成功和遗产在这笔赠款的未来三年资金周期（2019-2021）。 我们夏季学院在大数据中的总体目标是招募和培训下一代大型 数据科学家使用非传统的，基于动作的学习范式。这个六个星期的夏天 研究所将在国内和国际上招募约45名本科生，其中20个国内 受要求的SIBS资金机制和其他支持的学生支持的学生 机构和基础支持。我们建议使学员了解各种技术，技能和 健康大数据领域的问题。他们将由跨学科教师团队教授和指导 反映大数据研究所需的共同智力格局。他们将从事指导研究 健康大数据的三个主要领域的项目：电子健康记录/医学索赔，基因组学和 成像。某些项目将在心血管精度医学领域定义，由 高度定量研究人员的团队从事使用大数据的心血管研究。结论 在该计划中，将有一个结论性的顶峰研讨会，通过 海报和口头表现。 U-M研究人员，外部客人和专业人士都会有讲座 开发研讨会为学生准备研究生院。我们提出了一个Inter-SIBS协作 与Dordt College Summer计划的学员一起参加了这次结论研讨会。资源 为夏季研究所开发，包括讲座，作业，项目，模板代码和数据集 可以通过Wiki页面免费获得，以便可以在世界任何地方复制这种格式。这 民主传播计划将为本科生提供教学和培训材料 在世界各地的这个新领域。我们将提供多种专业发展机会和资源 为了向我们的学员准备研究生院，以便他们可以在高年级之前进行反思和计划。所有人 我们提出的活动反映了我们的三个特定目标：教学，指导和传播。