BIDS: Vanderbilt Training Program in BIg Biomedical Data Science

 描述（由申请人提供）：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，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。这些挑战包括确保云计算环境中的隐私和安全，建立定量分析工具的完整性和可重复性，以及证明用于解释生物医学背景下大数据的常见概率框架的有效性和普遍性。我们的社区可以通过发展一支将数据作为科学研究和工程师可扩展技术的劳动力来应对这些挑战。范德比尔特大学具有独特的优势，可以建立这样一个项目，并培养下一代数据科学领域最聪明的人才。该计划奠定了基础，并强调，生物医学信息学，计算机科学和生物统计学之间的共生关系。数据科学家必须在以下方面具有丰富的知识：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，模型建立和验证、错误发现率、缺失数据插补、测量误差的重新校准以及评估统计证据的强度）和3）科学方法以及导致数据采集、下游发现和下一代决策支持系统（管理结果和定量工具的可推广性）的特定生物医学和临床背景。由于这一领域正在迅速发展，为学生提供务实的培训环境，强调和发展批判性思维技能，并使他们接触到真实的系统中的现代生物医学数据分析，这是至关重要的。十多年来，范德比尔特大学的生物医学信息学博士课程为学生提供了这些经验，导致大数据分析的创新，对真实的临床环境中的基础科学应用产生了很大的影响。 尽管如此，还没有一个专门针对大数据科学的正式课程，学生可以在真实的生物医学合作的背景下学习这一领域（以前的学生已经通过善意和决心的拼凑来做到这一点，这在课程和艰苦的研究合作中必然是效率低下的）。该提案旨在利用范德比尔特在这一领域的优势，为下一代数据科学家建立大生物医学数据科学（BIDS）的范德比尔特培训计划。该计划将作为现有生物医学信息学博士课程的一个轨道进行管理，并由三位PI领导，他们在1）计算基础设施，2）统计方法学和3）NIH赞助的培训计划管理方面具有互补的专业知识。该计划的导师来自一个令人印象深刻的集合，建立了大学教师，并确保学生有接触到新的生物医学问题和跨学科的团队为基础的调查。

 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.