Biomedical Data Science Training Program

生物医学数据科学培训计划

• 批准号: 10428454
• 负责人: Paul B. Tchounwou
• 金额: $ 37.29万
• 依托单位: JACKSON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-08 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10428454
• 关键词: Achievement; Address; Administrative Supplement; Affect; Area; Artificial Intelligence; Behavioral Research; Big Data; Biomedical Research; Clinical Research; Code; Collaborations; Complex; Computational Technique; Data; Data Collection; Data Science; Data Scientist; Data Set; Development; Disease; Engineering; Faculty; Fostering; Galaxy; Goals; Health Sciences; Historically Black Colleges and Universities; Infrastructure; Institutes; Interdisciplinary Study; Machine Learning; Methodology; Minority Groups; Mississippi; Participant; Postdoctoral Fellow; Production; Public Health; Pythons; Research; Research Infrastructure; Research Personnel; Science; Scientist; Techniques; Technology; Trainers Training; Training; Training Activity; Training Programs; Translational Research; Underrepresented Populations; Universities; big biomedical data; biological research; biomedical data science; college; computing resources; health disparity; health management; improved; instructor; minority health; new technology; next generation; novel; programs; skills; stem; tool

PROJECT SUMMARY Jackson State University (JSU) proposes to develop a Biomedical Data Science Training Program (BDS-TP) to train the next generation of scientists to address the monumental challenge associated with the curation, integration, analysis, and interpretation of biomedical big data. This training program will strengthen the biomedical research infrastructure of the newly established RCMI Center for Health Disparities Research at JSU. With a special emphasis on the use and management of health disparities datasets representing multiple levels and domains of influence articulated in the National Institute of Minority Health and Health Disparities (NIMHD) Research Framework, the above-stated goal of the BDS-TP will be accomplished through two specific aims that include: 1) Forster Collaborations between biomedical data scientists and BDS-TP participants and RCHDR investigators. Approach: Identify and invite renowned scientists with expertise in specific areas of BDS to serve as leaders/instructors of specific BSD-TP training modules, and facilitate multidisciplinary collaborations; and 2) Build the capacity of JSU and other Mississippi’s HBCU- Historically Black Colleges and Universities (Alcorn State University, Mississippi Valley State University, and Tugaloo College) in biomedical data science. Approach: Organize Train-the-Trainer bootcamps to train the STEM (science, technology, engineering and technology) faculty and post-doctoral research fellows on how to apply cutting-edge data science techniques to different biomedical data types, how to use Galaxy and computational resources like Jupyter Hub, and coding with R, Python, and other data science tools such R-Studio, Galaxy, or Carpentries; and organize Codeathons focused on specific biomedical topics using various types of datasets. The achievements of these specific aims will significantly enhance the data science skills of program participants, ranging from basic best practices in data collection, curation and analysis to complex computational techniques like machine learning and artificial intelligence that are critical for advancing the science of minority health and health disparities.

项目摘要 杰克逊州立大学（JSU）提出的提案，旨在制定生物医学数据科学培训计划（BDS-TP） 培训下一代科学家，以应对与策展相关的巨大挑战， 生物医学大数据的整合，分析和解释。该培训计划将加强 新成立的RCMI健康差异研究中心的生物医学研究基础设施 JSU。特别着重于代表多个健康分布数据集的使用和管理 美国国家少数民族健康与健康差异所阐明的影响力和域 （NIMHD）研究框架，BDS-TP的上述目标将通过两个实现 包括：1）生物医学数据科学家与BDS-TP之间的Forster合作的具体目标 参与者和RCHDR调查人员。方法：确定并邀请拥有专业知识的著名科学家 BD的特定领域，以作为特定BSD-TP培训模块的领导者/讲师，并促进 多学科合作； 2）建立JSU和其他密西西比州的HBCU- 历史悠久的黑人学院和大学（密西西比州阿尔科恩州立大学，阿尔科恩州立大学 大学和Tugaloo College）生物医学数据科学。方法：组织训练训练 训练杆训练茎（科学，技术，工程和技术）教师和博士后 研究研究员如何将尖端数据科学技术应用于不同的生物医学数据类型，如何 使用Galaxy和计算资源（例如Jupyter Hub），并使用R，Python和其他数据进行编码 R-Studio，Galaxy或Carpentries等科学工具；并组织着专注于特定生物医学 使用各种类型的数据集的主题。这些特定目标的成就将显着增强 计划参与者的数据科学技能，从数据收集，策展和 对复杂的计算技术进行分析，例如机器学习和人工智能 为了推进少数民族健康和健康差异的科学。