Biomedical Big Data Training Program at UC Berkeley

 描述(由申请人提供)：这项建议回应了数据科学进步的迫切需要，以便下一代科学家拥有必要的技能，以利用前所未有的、不断增长的生物医学信息的数量和速度。大数据有望实现对健康和疾病机制的新理解，使生物医学科学发生革命性变化，使精准医学的重大挑战成为现实，并为社区和人口层面更有效的政策和干预铺平道路。这些突破需要训练有素的研究人员精通生物医学大数据科学，并拥有跨越传统学科边界进行有效合作的高级技能。为了应对这些挑战，加州大学伯克利分校提出了一项面向高级博士生的生物医学大数据创新培训计划。这笔培训补助金将支持6名学员。我们预计将进一步扩大我们的计划的覆盖范围，通过另类支持招收最多2名学生，从而使每年8名学生受益。参与的25名教师在生物医学应用方面拥有丰富的经验，并在生物统计学、因果推理、机器学习、大数据工具开发和可扩展计算方面拥有专业知识。这些课程共涉及8个系/学科：生物统计学、计算生物学、计算机科学、流行病学、综合生物学、分子与细胞生物学、神经科学和统计学。我们将从博士生中的任何一个或所有这些部门招募二年级或三年级的学员。那些被录取的人将参加为期一年的密集培训课程、研讨会和研讨会，从夏末的入门研讨会开始，以每个参与者在春季完成的顶峰项目结束。每个学员将被分配一名具有生物医学大数据科学专业知识的二级论文导师，与主要论文导师的专业知识相辅相成。专门培训将侧重于三个支柱：(1)将生物医学和实验知识以及感兴趣的科学问题转化为形式上的、现实的因果和统计估计问题；(2)可扩展的大数据计算；(3)具有因果和统计推理的有针对性的机器学习。活动将包括机器学习定向学习、统计编程和大数据计算方面的课程，以及由伯克利数据科学研究所、统计计算设施和伯克利研究计算领导的研讨会。Capstone课程将涉及生物医学科学方面的一个合作项目，涉及综合和综合应用受训人员在三个基础领域获得的技能。学员还将从小组研讨会、务虚会和跨学科培训中受益。 与干部和项目建立核心身份的会议。这一提议与加州大学伯克利分校的几项数据科学和精确医学倡议相吻合，并在理想的时机影响了向所有研究生教授数据科学的方式，重点放在校园内的生物医学研究上。

 DESCRIPTION (provided by applicant): This proposal responds to the urgent need for advances in data science so that the next generation of scientists has the necessary skills for leveraging the unprecedented and ever-increasing quantity and speed of biomedical information. Big Data hold the promise for achieving new understandings of the mechanisms of health and disease, revolutionizing the biomedical sciences, making the grand challenge of Precision Medicine a reality, and paving the way for more effective policies and interventions at the community and population levels. These breakthroughs require highly trained researchers who are proficient in biomedical Big Data science and have advanced skills at collaborating effectively across traditional disciplinary boundaries. To meet these challenges, UC Berkeley proposes an innovative training program in Biomedical Big Data for advanced Ph.D. students. This training grant will support 6 trainees. We anticipate further extending the reach of our program by admitting up to 2 additional students on alternative support, thus benefitting 8 students per year. The 25 participating faculty have extensive experience with biomedical applications and expertise in biostatistics, causal inference, machine learning, the development of Big Data tools, and scalable computing. Together, they span 8 departments/programs: Biostatistics; Computational Biology; Computer Science; Epidemiology; Integrative Biology; Molecular & Cell Biology; Neuroscience; and Statistics. We will recruit participants from Ph.D. students in their second or third year of study in any/all of these departments. Those accepted into the program will participate in an intensive year of training courses, seminars, and workshops, beginning with introductory seminars in late summer and ending with a capstone project by each participant in the spring. Each trainee will be assigned a secondary thesis advisor with biomedical Big Data science expertise complementing that of the primary thesis advisor. Specialized training will focus on three pillars: (1) translation of biomedical and experimental knowledge and scientific questions of interest into formal, realistic problems of causal and statistical estimation; (2) scalable Big Data computing; and (3) targeted machine learning with causal and statistical inference. Activities will include courses in machine learning targeted learning, statistical programming, and Big Data computing, as well as workshops led by the Berkeley Data Science Institute, Statistical Computing Facility, and Berkeley Research Computing. The capstone course will involve a collaborative project in biomedical science involving the integrated and combined application of skills acquired by the trainees in the three foundational areas. Trainees will also benefit from group seminars, retreats, and interdisciplinary meetings that build a core identity with the cadre and the program. This proposal dovetails with several data science and precision medicine initiatives at UC Berkeley and comes at an ideal time to influence how data science is taught to all graduate students, focusing on biomedical research across campus.