Biomedical Big Data Training Program at UC Berkeley

 该提案响应了对数据科学进步的迫切需求，以便下一代科学家拥有利用前所未有的和不断增加的生物医学信息数量和速度的必要技能。大数据有望实现对健康和疾病机制的新理解，彻底改变生物医学科学，使精准医疗的巨大挑战成为现实，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，他们精通生物医学大数据科学，并具有跨越传统学科界限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个创新的生物医学大数据培训计划，为高级博士。学生这笔培训补助金将资助6名受训人员。我们预计将进一步扩大我们的计划的覆盖面，通过接纳多达2名额外的学生替代支持，从而每年受益8名学生。25名参与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。他们一起跨越8个部门/计划：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。我们将从博士生中招募参与者学生在他们的第二或第三年的研究在任何/所有这些部门。被录取的人将参加一年的密集培训课程，研讨会和讲习班，从夏末的介绍性研讨会开始，到春季每个参与者的顶点项目结束。每个学员将被分配一个具有生物医学大数据科学专业知识的二级论文顾问，以补充主要论文顾问的专业知识。专业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因果和统计估计的正式，现实问题;（2）可扩展的大数据计算;（3）有针对性的机器学习因果和统计推断。活动将包括机器学习目标学习，统计编程和大数据计算课程，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。顶点课程将涉及生物医学科学的一个合作项目，涉及学员在三个基础领域获得的技能的综合和综合应用。学员还将受益于小组研讨会，务虚会，和跨学科 与干部和项目建立核心身份的会议。该提案与加州大学伯克利分校的几项数据科学和精准医学计划相吻合，并在一个理想的时间来影响如何向所有研究生教授数据科学，重点是校园内的生物医学研究。

 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.