Biomedical Informatics and Data Science Training at Yale

耶鲁大学生物医学信息学和数据科学培训

• 批准号: 9531731
• 负责人: CYNTHIA A. BRANDT
• 金额: $ 10万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9531731
• 关键词: Area; Bioinformatics; Biological Sciences; Clinical; Clinical Informatics; Clinical Research; Computational Biology; Computer Simulation; Data Science; Data Set; Development; Disease model; Doctor of Philosophy; Doctor' s Degree; Educational Curriculum; Enrollment; Environment; Faculty; Goals; Grant; Health Occupations; Health Sciences; Healthcare; Informatics; Interdisciplinary Study; Medical Informatics; Medical center; Mentors; Postdoctoral Fellow; Process; Research; Research Project Grants; Science; Students; Supervision; Training; Training Programs; base; biomedical informatics; career; experience; interest; post-doctoral training; pre-doctoral; programs; translational genomics

PROJECTSUMMARY/ABSTRACT The Yale Biomedical Informatics and Data Science Training Program—directed by Profs. Cynthia Brandt and Michael Krauthammer— is based in the Yale Center for Medical Informatics (YCMI) and other academic units at Yale, reflecting the diversity of Yale’s collaborative Biomedical Informatics research environment. Our training focuses on the following informatics areas: 1) health care/clinical informatics, 2) translational bioinformatics, and 3) clinical research informatics. Active research projects span a broad spectrum, from clinical decision support, to the development of new statistical and bioinformatics approaches in translational genomics, to computational modeling of disease processes. The scope of Biomedical Informatics activities is growing rapidly at Yale, with emerging opportunities in interdisciplinary projects tackling large biomedical data sets. Accordingly, the grant features mentors with expertise in Data Science, a curriculum with an elective focus on Biomedical Data Science, and provides training in interdisciplinary research. Predoctoral training is carried out primarily in Yale’s interdepartmental PhD program in Computational Biology and Bioinformatics (CBB), which was inaugurated in 2003 and recently accepted its fourteenth class of students. Postdoctoral fellows with a doctoral degree in the health professions or in an area of science other than informatics may enroll in one of two research-oriented graduate programs: studying for an MS or PhD degree in CBB or for a Master of Health Science (MHS) degree in Yale's Clinical Informatics Track. For postdoctoral trainees who already have a doctoral degree in informatics or a closely related field, degree training may not be needed or appropriate. Postdoctoral training involves defining one or two research projects which can be carried out independently, under faculty supervision. Depending on their specific backgrounds and interests, postdoctoral fellows are encouraged to take part in in a variety of other activities, including participation in institutional computing activities in both the clinical and bioscience arenas. The overall goal is to provide all trainees with the necessary background and experience that will allow them to pursue productive academic and research careers in Biomedical Informatics and Data Science. We are requesting support for 9 predoctoral trainees and 6 postdoctoral trainees.

项目概要/摘要 耶鲁大学生物医学信息学和数据科学培训计划——由 Cynthia Brandt 教授和 Michael Krauthammer — 位于耶鲁大学医学信息学中心 (YCMI) 和其他学术单位 耶鲁大学，反映了耶鲁大学生物医学信息学合作研究环境的多样性。 重点关注以下信息学领域：1）医疗保健/临床信息学，2）转化生物信息学，以及 3）临床研究信息学。 活跃的研究项目涵盖广泛，从临床决策支持到新药物的开发 转化基因组学中的统计和生物信息学方法，用于疾病的计算模型 耶鲁大学生物医学信息学活动的范围正在迅速扩大，并带来了新的机会。 在跨学科项目中，谈判大型生物医学数据集因此，该资助的特色是导师与。 数据科学方面的专业知识，课程重点关注生物医学数据科学，并提供培训 跨学科研究的博士前培训主要在耶鲁大学的跨院系博士项目中进行。 计算生物学和生物信息学 (CBB) 博士学位，该学位于 2003 年成立，最近接受了其 第十四届博士后，具有卫生专业或某一领域的博士学位。 信息学以外的科学专业可以报名参加两个研究型研究生课程之一： CBB 硕士或博士学位或耶鲁大学临床信息学方向的健康科学硕士学位 (MHS)。 对于已获得信息学或密切相关领域博士学位的博士后学员，学位 博士后培训可能不需要或不适合定义一两个研究项目。 这可以在教师的监督下独立进行，具体取决于他们的具体背景和情况。 兴趣，鼓励博士后研究员参加各种其他活动，包括参与 临床和生物科学领域的机构计算活动的总体目标是提供所有。 受训者具有必要的背景和经验，使他们能够追求富有成效的学术和 我们正在寻求对 9 名博士前研究人员的支持。 硕士研究生6名，博士后6名。