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.

Public Health Relevance

Biomedical Informatics focuses on the creative use of computers in support of clinical medicine, biomedical research, and medical education, and Biomedical Data Science is a field concerned with analyzing and deriving knowledge from increasingly large data sets that are being collected across every field of clinical medicine and the biosciences. The goal of Yale's Biomedical Informatics and Data Science training program is to provide graduate students and postdoctoral fellows with coursework and experience that will allow them to pursue productive research careers in Biomedical Informatics and Data Science broadly defined. 1

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
2T15LM007056-31
Application #
9263063
Study Section
Special Emphasis Panel (ZLM1)
Program Officer
Florance, Valerie
Project Start
1987-07-01
Project End
2022-06-30
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
31
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Yale University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Zawack, Kelson; Love, Will; Lanzas, Cristina et al. (2018) Inferring the interaction structure of resistance to antimicrobials. Prev Vet Med 152:81-88
Abel, Erica A; Shimada, Stephanie L; Wang, Karen et al. (2018) Dual Use of a Patient Portal and Clinical Video Telehealth by Veterans with Mental Health Diagnoses: Retrospective, Cross-Sectional Analysis. J Med Internet Res 20:e11350
Klein, Michael I; Stern, David F; Zhao, Hongyu (2017) GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics 18:317
Lu, Qiongshi; Powles, Ryan L; Abdallah, Sarah et al. (2017) Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLoS Genet 13:e1006933
McGregor, Kyle A; Hensel, Devon J; Waltz, Amy C et al. (2017) Adolescent Sexual Behavior Research: Perspectives of Investigators, IRB Members, and IRB Staff about Risk Categorization and IRB Approval. IRB 39:17-20
McDougal, Robert A; Morse, Thomas M; Carnevale, Ted et al. (2017) Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. J Comput Neurosci 42:1-10
Gaines, J C; Virrueta, A; Buch, D A et al. (2017) Collective repacking reveals that the structures of protein cores are uniquely specified by steric repulsive interactions. Protein Eng Des Sel 30:387-394
Heffelfinger, Christopher; Fragoso, Christopher A; Lorieux, Mathias (2017) Constructing linkage maps in the genomics era with MapDisto 2.0. Bioinformatics 33:2224-2225
Lin, Zhongwei; Tropper, Carl; Mcdougal, Robert A et al. (2017) Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons. ACM Trans Model Comput Simul 27:
Lin, Zhongwei; Tropper, Carl; Yao, Yiping et al. (2017) Load balancing for multi-threaded PDES of stochastic reaction-diffusion in neurons. J Simul 11:267-284

Showing the most recent 10 out of 231 publications