BIDS: Vanderbilt Training Program in BIg Biomedical Data Science

 描述(申请者提供)：信息和高通量技术的进步为生物医学的大数据时代奠定了基础。然而，仍然存在尚未解决的挑战，这些挑战可能会限制大数据探索在基础、临床和生物医学科学中的影响。这些挑战从确保云计算环境中的隐私和安全，到建立定量分析工具的完整性和可重复性，到证明用于在生物医学背景下解释大数据的常见概率框架的有效性和普适性。我们的社区可以通过培养一支将数据作为一门科学来研究并设计可扩展技术的劳动力来为这些挑战做好准备。范德比尔特大学在建立这样一个项目并培养下一代最聪明的数据科学人才方面处于独特的地位。拟议的计划奠定了生物医学信息学、计算机科学和生物统计学之间的基础，并强调了它们之间的共生关系。数据科学家必须精通以下方面：1)用于大规模收集、处理和分析数据的计算技术、技术和基础设施；2)适应大规模、复杂、高维生物医学数据的统计方法(例如，建模和验证、错误发现率、缺失数据补偿、测量误差重新校准，以及评估统计证据的强度)；3)导致数据捕获、下游发现和下一代决策支持系统(管理结果和量化工具的普适性)的科学方法和特定的生物医学和临床环境。由于这一领域发展迅速，为学生提供强调和发展批判性思维技能的务实培训环境，并让他们接触到真实系统中的现代生物医学数据分析，是至关重要的。十多年来，范德比尔特大学的生物医学信息学博士项目为学生提供了这些经验，导致了大数据分析方面的创新，对真实临床环境中的基础科学应用具有很高的影响。 尽管如此，没有专门针对大数据科学的正式项目，学生可以在真正的生物医学合作的背景下学习这一领域(以前的学生通过善意和决心的拼凑成功地做到了这一点，这在课程学习和艰苦的研究合作中肯定是低效的)。这项提议寻求在范德比尔特在这一领域的实力的基础上，为下一代数据科学家建立范德比尔特大生物医学数据科学(BIDS)培训计划。该计划将作为现有生物医学信息学博士计划的一个轨道进行管理，并由三个PI领导，他们在1)计算基础设施、2)统计方法和3)NIH赞助的培训计划的管理方面具有互补的专业知识。该项目的导师来自一批令人印象深刻的知名大学教师，并确保学生接触到新的生物医学问题和基于跨学科团队的调查。

 DESCRIPTION (provided by applicant): Advances in information and high-throughput technologies have set the stage for the 'big data age' in biomedicine. However, there remain unresolved challenges that could limit the impact of big data exploration in the basic, clinical an biomedical sciences. These challenges range from assuring privacy and security in cloud computing environments to establishing the integrity and reproducibility of quantitative analysis tools to proving the validity and generalizability of common probabilistic frameworks used to interpret big data in a biomedical context. Our community can prepare for these challenges by developing a workforce that studies data as a science and engineers scalable technologies. Vanderbilt University is uniquely positioned to establish such a program and train the next generation of brightest minds in data science. The proposed program lays a foundation in, and emphasizes the symbiotic relationship between, biomedical informatics, Computer Science, and Biostatistics. Data scientists must be highly knowledgeable in 1) computational techniques, technologies, and infrastructure for collecting, processing, and analyzing data on a massive scale, 2) statistical methodologies that accommodate large-scale, complex, high-dimensional biomedical data (e.g., model building and validation, false discovery rates, missing data imputation, recalibration for measurement error, and assessing the strength of statistical evidence) and 3) the scientific method and the specific biomedical and clinical contexts that led to data capture, downstream discovery and next-generation decision support systems (which governs the generalizability of results and quantitative tools). Because this field is evolving quickly, it is paramount to provide students with pragmatic training environments that emphasize and develop critical thinking skills and expose them to modern biomedical data analysis in real systems. For over a decade, the biomedical informatics doctoral program at Vanderbilt University has provided students with these experiences, leading to innovations in big data analytics with high impact in the underlying scientific applications in real clinical environments. Despite this, there is no formal program dedicated to big data science where students can study this area in the context of real biomedical collaborations (previous students have managed to do this via a patchwork of goodwill and determination, which is necessarily inefficient in coursework and laborious research collaborations). This proposal seeks to build on Vanderbilt's strength in this area to establish the Vanderbilt Training Program in Big Biomedical Data Science (BIDS) for the next generation of data scientists. This program will be managed as a track within the existing biomedical informatics doctoral program and led by the three PI's with complementary expertise in 1) computational infrastructure, 2) statistical methodologies, and 3) management of NIH-sponsored training programs. The program's mentorship comes from an impressive collection of well-established university faculty and ensures students have exposure to novel biomedical problems and interdisciplinary team-based investigations.