PREDOCTORAL TRAINING IN BIOMEDICAL BIG DATA SCIENCE

生物医学大数据科学博士前培训

• 批准号: 9116413
• 负责人: Michael J Daniels
• 金额: $ 22.13万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9116413
• 关键词: Data Science; Training; pre-doctoral

 DESCRIPTION (provided by applicant): The ever-increasing accumulation of data continues to outstrip the graduate training needed to meaningfully mine the data collected. This issue is further complicated by the fact that holistic training in biomedical big data analysis requires PhD level expertise in not one, but three core research areas: (1) biology (2) statistics and (3) computer science, yet the majority of traditional PhD training programs demand that students choose just one of these areas as their focus. A growing number of biomedical PhD students are recognizing the need to develop data analysis and computational biology skills, at the same time that a growing number of computer science and statistics PhD students are realizing that their marketability could be substantially expanded if they knew how to apply their skills to solve outstanding problems in the health arena. The purpose of this pre-doctoral training program we are proposing to introduce at The University of Texas at Austin is for the trainee to become an expert in one of the following areas: 1. Statistics (STAT); 2. Computer Science (CS); 3. Computational science, engineering, and mathematics (CSEM); or 4. Biology (via a PhD in one of a. neuroscience [NS]; b. ecology, evolution, and behavior [EEB]; c. cell and molecular biology [CMB]; or d. Biomedical Engineering [BME]) while also obtaining essential training in all three core areas (statistics, computer science, and biology). This will ideally equip the graduates from this program to make important scientist c discoveries using big data. The challenge is in developing a program that trains these multidisciplinary skills without sacrificing strength in ther core PhD area. This is an exciting opportunity for the new PhD program in statistics and the already established PhD programs involved, and it is consistent with the interdisciplinary emphasis of all the faculty involved with this application. This training program will differ from he standard training programs at UT- Austin by incorporating new courses, a new seminar/workshop, and program-specific rotations during year 3. These rotations will provide opportunities for trainees to work in research labs in the new University of Texas at Austin Dell Medical School and the Dell Pediatric Research Institute. Research at the interface of these three areas requires excellent collaborative skills. In addition to subject matter training, we wil help trainees develop strong oral and written communication skills. This combination of knowledge and communication will equip the trainees to make major contributions to big data biomedical science. We anticipate funding five trainees per year. Trainees will formally start the training program during year 2 of their PhD programs.

 描述（由申请人提供）：不断增加的数据积累继续超出了有意义地挖掘所收集数据所需的研究生培训的水平，因为生物医学大数据分析的整体培训需要博士学位，这一事实使这个问题变得更加复杂。 博士生的专业知识不是一个而是三个：（1）生物学（2）统计学和（3）计算机科学，但大多数传统的博士培训项目要求学生只选择这些领域之一作为他们的重点。越来越多的生物医学博士生认识到培养数据分析和计算生物学技能的必要性，同时越来越多的计算机科学和统计学博士生意识到，如果他们知道如何运用自己的技能，他们的市场竞争力可能会大幅扩大解决 我们建议在德克萨斯大学奥斯汀分校引入这一博士前培训计划的目的是让学员成为以下领域之一的专家： 1. 统计学（STAT）； 2. 计算机科学 (CS)； 3. 计算科学、工程和数学 (CSEM)；或 4. 生物学（获得 a. 神经科学 [NS]；b. 生态学、进化和行为 [EEB] 之一的博士学位）细胞和分子生物学； [CMB]；或 d. 生物医学工程 [BME]），同时获得所有三个核心领域（统计学、计算机科学和生物学）的必要培训，这将使该项目的毕业生能够利用大数据做出重要的科学家发现。面临的挑战是开发一个在不牺牲其核心博士领域实力的情况下培养这些多学科技能的计划，这对于新的统计学博士课程和已建立的博士课程来说是一个令人兴奋的机会，并且它与跨学科重点是一致的。全体教员中该培训计划与 UT-奥斯汀分校的标准培训计划不同，在第 3 年纳入了新课程、新的研讨会/讲习班和特定计划的轮换。这些轮换将为学员提供从事研究工作的机会。德克萨斯大学奥斯汀戴尔​​医学院和戴尔儿科研究所的实验室在这三个领域的交叉领域进行研究需要出色的协作技能除了主题培训之外，我们还将帮助学员培养强大的口头和书面沟通技巧。 .这种知识的结合和沟通将使学员能够为大数据生物医学科学做出重大贡献，我们预计每年资助五名学员将在博士课程的第二年正式开始培训计划。