iDISCOVER: Integrated Data Science Training in CardioVascular Medicine

翻译后摘要：这个新的T32提案将支持研究生和博士后研究员追求综合 加州大学洛杉矶分校心血管（CV）医学的数据科学培训。综合数据科学培训计划 在当今的CV生物医学界非常有限。CV医学面临的数据科学挑战有很多方面 独特的，并有长期的历史记录的信息和数据。心血管疾病是慢性、异质性的 表现出不同的时间分布与多器官改变相结合的疾病，需要新的 整合跨不同信息的扩展连续体的发现的分析平台（例如，文本、图像、 组学）。CV数据集的复杂性和大小已经将计算方法推向了极限，因此 降低了CV药品的增值率。为此，大家有一个广泛的共识，就是我们必须合并 数据科学与CV医学。创造下一代劳动力， 理解解决真实世界CV问题的数据科学策略将最终实现精确CV 药我们的T32填补了CV数据科学的独特利基，目前在加州大学洛杉矶分校和全国都没有。 加州大学洛杉矶分校CV医学综合数据科学培训（iDISCOVER）计划吸引了来自 加州大学洛杉矶分校医学和工程学院，为致力于密集数据的学员建立一个计划， CV医学的科学应用。我们在建立培训计划方面有着可靠的记录， 由我们的NIH大数据到知识（BD 2K）倡议，加州大学洛杉矶分校心脏BD 2K中心证明。经验 使我们能够构建T32研究计划，针对CV中最紧迫的数据科学问题 药我们为期两年的计划将接受合格的学生谁已经完成了第一年的博士培训，从 计算机科学（CS），生物信息学（BI）或生物工程（BE）;以及来自精英简历的合格博士后研究员 程序.我们将在博士培训的第二年和第三年培训博士预科学生， 研究员在他们的研究金的第一年和第二年。受训人员将参加高级课程， 具体重点领域：（一）组学表型支持的结果研究;（二）机器学习支持的 方法在CV医学;和（iii）信息检索和知识库建设。学员将参与 在CV临床轮换中，让他们接触到紧迫的CV数据科学问题。学员将接受以下指导： 共同指导安排（1名简历导师和1名数据科学导师）。我们有一个出色的14人小组 核心教师和6名临床支持教师在加州大学洛杉矶分校的医学，工程和生命学院 以理工科为重我们的教师已经建立，充满活力和资金充足的研究计划，具有强大的历史， 引导学生走向成功的职业生涯。我们的项目促进了对代表性不足的少数群体的培训， 所有教员的培训记录都证明了这一点。我们的iDISCOVER计划得到了以下机构的支持： 医学院、工程学院、格拉德学院、系。CS、BI和BE以及心脏病学和 Physiology.这些要素确保为方案的实施、推进和成功提供坚实的支持。

Abstract: This new T32 proposal will support graduate students and postdoctoral fellows pursuing integrated data science training in cardiovascular (CV) medicine at UCLA. Integrated data science training programs are very limited in today’s CV biomedical community. Data science challenges facing CV medicine are in many ways unique and have a long historic track record of information and data. CV diseases are chronic, heterogeneous disorders that exhibit distinct temporal profiles combined with multi-organ alterations, necessitating novel analysis platforms that integrate findings across expanded continuums of diverse information (e.g., text, imaging, omics). The complexity and size of CV datasets have pushed computational approaches to their limits, thus attenuating the rate for adding value in CV medicine. To this end, there is broad consensus that we must merge data science with CV medicine. The creation of a next-generation workforce having more advanced understanding of data science tactics for addressing real-world CV problems will ultimately realize precision CV medicine. Our T32 fills a unique niche in CV data science that is currently missing, both at UCLA and nationally. The UCLA Integrated Data Science Training in CV Medicine (iDISCOVER) Program draws upon faculty from the UCLA Schools of Medicine and Engineering, to establish a program for trainees committed to intensive data science applications in CV medicine. We have a substantiated track record in establishing training programs, as evidenced by our NIH Big Data to Knowledge (BD2K) Initiative, Heart BD2K Center at UCLA. Experience has enabled us to construct a T32 research program targeting the most pressing data science questions in CV medicine. Our two-year program will accept qualified students who have completed 1st year PhD training from Computer Science (CS), Bioinformatics (BI) or Bioengineering (BE); and eligible postdoc fellows from elite CV programs. We will train predoctoral students during their second and third year of PhD training, and postdoctoral fellows during their first and second year of their fellowship. Trainees will engage in advanced coursework within the specific focus areas: (i) omics phenotyping-supported outcome studies; (ii) machine learning-supported approaches in CV medicine; and (iii) information indexing and knowledgebase construction. Trainees will engage in CV clinical rotations to give them exposure to pressing CV data science questions. Trainees will be guided by a co-mentoring arrangement (1 CV mentor and 1 data science mentor). We have an outstanding group of 14 core faculty and 6 clinical supporting faculty members in the UCLA Schools of Medicine, Engineering and Life Sciences. Our faculty have established, vibrant and well-funded research programs with strong histories of guiding students to successful careers. Our program promotes the training of underrepresented minority groups, as demonstrated by training records of all faculty. Our iDISCOVER program has the institutional backing from Schools of Medicine, Engineering, Grad Division, Depts. of CS, BI, and BE, as well as Cardiology and Physiology. These elements ensure solid support for program implementation, advancement and success.