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过有意义地挖掘收集的数据所需的研究生培训。这个问题进一步复杂化的事实是，在生物医学大数据分析的整体培训需要博士学位 博士生的专业水平不是一个，而是三个核心研究领域：（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计划要求学生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需要发展数据分析和计算生物学技能，同时越来越多的计算机科学和统计学博士生意识到，如果他们知道如何应用他们的技能来解决问题，他们的市场可能会大大扩大。 卫生竞技场存在的突出问题。我们建议在德克萨斯大学奥斯汀分校推出的这个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（STAT）;计算机科学（CS）; 3.计算科学，工程和数学（CSEM）;或4。生物学（通过博士学位在一个。神经科学[NS]; B.生态学、进化和行为[EEB]; c.细胞和分子生物学[CMB];或d.生物医学工程[BME]），同时还获得所有三个核心领域（统计，计算机科学和生物学）的基本培训。这将使该计划的毕业生能够利用大数据做出重要的科学发现。面临的挑战是在开发一个程序，培训这些多学科的技能，而不牺牲在其他核心博士领域的实力。这是一个令人兴奋的机会，为新的博士课程在统计和已经建立的博士课程参与，它是与所有参与此应用程序的教师的跨学科重点一致。该培训计划将与德克萨斯大学奥斯汀分校的标准培训计划不同，其中纳入了新课程、新的研讨会/研讨会以及第3年期间特定于计划的轮换。这些轮岗将为学员提供在新的德克萨斯大学奥斯汀分校戴尔医学院和戴尔儿科研究所的研究实验室工作的机会。在这三个领域的接口研究需要优秀的协作技能。除了主题培训外，我们还将帮助学员培养较强的口头和书面沟通能力。这种知识和沟通的结合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5名受训人员。学员将在博士课程的第二年正式开始培训计划。