iDISCOVER: Integrated Data Science Training in CardioVascular Medicine

摘要：这项新的T32提案将支持研究生和博士后从事综合 加州大学洛杉矶分校心血管(CV)医学数据科学培训。综合数据科学培训计划有 在当今的简历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 独一无二，拥有长期的信息和数据历史记录。心血管疾病是慢性的、异质性的。 表现出不同的时间轮廓并结合多器官改变的疾病，需要新的 跨不同信息(例如，文本、图像、 经济学)。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 降低循环医学的增值率。为此，我们必须合并是一个广泛的共识。 数据科学与CV医学。创造更先进的下一代劳动力 理解解决现实世界简历问题的数据科学策略将最终实现精确的简历 医药。我们的T32填补了目前加州大学洛杉矶分校和全国范围内缺失的简历数据科学领域的一个独特的利基市场。 加州大学洛杉矶分校的CV医学综合数据科学培训(IDiscover)项目吸引了来自 加州大学洛杉矶分校医学院和工程学院，为致力于密集数据的学员建立一个计划 在心血管医学中的科学应用。我们在制定培训计划方面有可靠的记录，如 加州大学洛杉矶分校的心脏BD2K中心证明了我们的NIH大数据到知识(BD2K)倡议。经验告诉我们 使我们能够构建针对简历中最紧迫的数据科学问题的T32研究计划 医药。我们为期两年的项目将招收完成第一年博士培训的合格学生 计算机科学(CS)、生物信息学(BI)或生物工程(BE)；以及精英简历中合格的博士后研究员 程序。我们将在博士后培训的第二年和第三年培养博士后，以及博士后 研究员在他们第一年和第二年的团契。学员将在以下时间内参加高级课程 具体的重点领域：(一)组学表型支持的结果研究；(二)机器学习支持的结果研究 (3)信息标引和知识库建设。实习生将参与 在简历临床轮换中，让他们接触到紧迫的简历数据科学问题。学员将在以下人员的指导下 共同指导安排(1名简历导师和1名数据科学导师)。我们有一支出色的14人团队 加州大学洛杉矶分校医学、工程和生活学院的核心教师和6名临床辅助教师 科学。我们的教员建立了充满活力的、资金充足的研究项目，拥有强大的 引导学生走向成功的职业生涯。我们的计划促进了对代表不足的少数群体的培训， 所有教职员工的培训记录都证明了这一点。我们的iDiscover计划得到了来自 医学院，工程，研究生部，系。CS、BI和BE，以及心脏病学和 生理学。这些要素确保为方案的实施、进展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.