Biomedical Big Data Training Grant

生物医学大数据培训补助金

• 批准号: 9062411
• 负责人: Matteo Pellegrini
• 金额: $ 26.39万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9062411
• 关键词: Grant; Training; big biomedical data

 DESCRIPTION (provided by applicant): This broad-based T32 proposal is focused on supporting graduate students pursuing Biomedical Big Data Analysis research at UCLA. Over the past few years there has been increasing recognition that the biomedical sciences are undergoing a transformation, led by the development of new technologies that have enormously increased the capacity to generate data. These include technologies to sequence DNA and RNA; measure protein and metabolite abundances using mass spectrometry; as well as multiple other high throughput platforms for screening and phenotyping. Coupled with the advances in medical imaging and EHRs, the amount of data is growing faster than ever before. While several other training grants exist at UCLA, none are specifically focused on the general problem of analyzing and relating large biomedical data sets. Thus this new training grant we propose here fills a critical niche that will allow us to support graduate students in fundamental aspects of biomedical "big data" analysis. This effort realizes the novel development of a tailored set of courses in big data analysis, along with specialized team building activities in bi data challenges and extramural internships in big data centers. This training program will position our students for the future growth in big data science, fostering the growth of this critial area at UCLA. We posit that this program will be critical for the growth of our bioinformatics program and big data biomedical science at UCLA. Faculty supporting this training program represent an interdisciplinary group of researchers from across the UCLA campus, including the Schools of Engineering & Applied Sciences; College of Life Sciences and the Medical School. Many of the faculty are nationally recognized leaders in their disciplines; collectively, these individuals provide a comprehensive and complementary set of (funded) research areas and skills that enrich the training experience.

 描述（由申请人提供）：这项基础广泛的 T32 提案重点支持研究生在加州大学洛杉矶分校进行生物医学大数据分析研究。在过去的几年里，人们越来越认识到生物医学正在经历一场变革，新技术的发展极大地提高了生成数据的能力。其中包括 DNA 和 RNA 测序技术；使用质谱法测量蛋白质和代谢物丰度；以及多个其他用于筛选和表型分析的高通量平台。再加上医学成像和电子病历的进步，数据量的增长速度比以往任何时候都快。虽然加州大学洛杉矶分校还存在其他几项培训资助，但没有一项专门针对分析和关联大型生物医学数据集的一般问题。因此，我们在这里提出的这项新的培训补助金填补了一个关键的空白，使我们能够在生物医学“大数据”分析的基本方面为研究生提供支持。这项工作实现了大数据分析定制课程的新颖开发，以及双数据挑战和大数据中心校外实习的专业团队建设活动。该培训计划将为我们的学生奠定大数据科学未来发展的基础，促进加州大学洛杉矶分校这一关键领域的发展。我们认为该项目对于加州大学洛杉矶分校生物信息学项目和大数据生物医学科学的发展至关重要。支持该培训计划的教师代表来自加州大学洛杉矶分校校园的跨学科研究人员小组，包括工程与应用科学学院；生命科学学院和医学院。许多教师都是各自学科领域全国公认的领导者；总的来说，这些人提供了一套全面且互补的（受资助的）研究领域和技能，丰富了培训经验。