Bio-Data Science Training Program

 描述(由申请人提供)：数据生成、获取、管理、分析、可视化和解释方面的问题，这些问题一直是生物医学科学中的重要参与者，现在在理解健康和疾病的巨大努力中发挥着主导作用。大型生物医学数据前所未有的规模、复杂性和异构性要求我们进行研究，使我们能够更有效地从数据中提取知识，以便做出更好的预测，表征生物系统，并通常使后续研究成为可能。针对这些问题的研究活动是多个学科的融合：计算机科学、统计学/生物统计学和特定的生物医学科学领域都提供了对生物医学数据科学的关键见解，或我们在本提案中所称的生物数据科学。这门科学影响基础生物学研究以及转译研究和临床研究。利用华盛顿大学现有的博士项目、协作研究基础设施以及在数据科学和大数据方面的各种倡议，我们建议为博士生预科学生提供生物数据科学方面的交叉培训。学员将来自三个重点领域之一，将完成这三个领域的课程工作，并将接受跨学科研究、计算机基础设施以及在博士论文工作中发现新知识的负责任的研究行为方面的培训。

 DESCRIPTION (provided by applicant): Problems in data generation, acquisition, management, analysis, visualization, and interpretation, which have always been important players in biomedical science, now assume leading roles in the massive effort to understand health and disease. The unprecedented size, complexity, and heterogeneity of big biomedical data demands research that will allow us to more efficiently extract knowledge from data in order to make better predictions, to characterize biological systems, and generally to enable subsequent investigation. The research activity directed towards these problems is an amalgamation of multiple disciplines: computer sciences, statistics/biostatistics, and specific biomedical science areas all offer critical insights into biomedical data science, or what we call bio-data science in this proposal. This science affects basic biological investigations as well as translational studies and clinical research. Taking advantage of standing PhD programs, the collaborative research infrastructure, and various initiatives in data science and big data at UW, we propose cross training in bio-data science for pre-doctoral students. Trainees will come from one of three focus areas, will complete course work in the three areas, and will be trained in interdisciplinary research, computing infrastructure, and the responsible conduct of research on their way to discovering new knowledge in their PhD thesis work.