The Colorado Biomedical Informatics Training Program is an independent, Ph.D.- granting and postdoctoral training program based in the University of Colorado School of Medicine, with a 10 year track record of innovative and effective training of pre- and post-doctoral fellows for research careers. We are a second-generation teaching program, informed by the experience of the many biomedical informatics training models that have come before us. Our program is designed to produce graduates with depth in both computational methods and biomedicine, an intimate familiarity with the science and technology that synergizes the two, and the skills necessary to pioneer novel computational approaches to significant biomedical questions. We are aware of the difficulty of achieving both breadth and depth in a reasonable amount of time, and believe we have identified a novel approach that is capable of training productive interdisciplinary scientists in a relatively short period. The program is tightly focused on transforming already strong students and recent Ph.D.'s into mature and productive scientists. Our program is structured around a set of four categories of educational goals and objectives: knowledge, communication skills, professional behavior, and self- directed life-long learning. Our graduates demonstrate the knowledge of core concepts and principles of biomedical informatics, and have the ability to apply computation to gain insight into important biomedical problems. Their knowledge includes mastery of the fundamentals of biomedicine, clinical and translational research, statistics and computer science, as well as proficiency in the integration of these fields. Our graduates have contributed to the discovery and dissemination of new knowledge. They demonstrate interpersonal, oral and written skills that enable them to interact productively with scientists from both biomedical and computational domains, to communicate the results of their work in appropriate formats, and to teach others biomedical informatics skills;they effectively bridge the gap between biomedical and computational cultures. Our graduates demonstrate the highest standards of professional integrity and exemplary behavior, as reflected in a commitment to the ethical conduct of research, continuous professional development and thoughtfulness regarding the broader implications of their work. Our graduates demonstrate habits and skills for self-directed and life-long learning, and recognize that biomedical informatics is a rapidly evolving discipline. Our program itself is also undergoing continuous improvement, carefully tracking our efforts and quickly responding to changes in the field and our situation. We are justifiably proud of our outstanding track record as well as our dynamic and adaptive approach to the training of adept, flexible and curious scientists able to comfortably assimilate new ideas and technologies during the course of their professional careers. During the next period, we propose to develop a training track in Clinical Research informatics, to complement our existing training in Translational Informatics. Our training grant supports six predoctoral and three postdoctoral trainees;it is currently supplemented by an ARRA-related award of an additional one predoctoral and three postdoctoral slots. Based on our successful track record, we are requesting that the ARRA supplemental slots be continued, the addition of one predoctoral and one postdoctoral slot, and the creation of four short term diversity-related positions, for a total request of 8 predoctoral, 7 postdoctoral and 4 short term positions.

Public Health Relevance

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

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
5T15LM009451-08
Application #
8681518
Study Section
Special Emphasis Panel (ZLM1)
Program Officer
Florance, Valerie
Project Start
2007-07-01
Project End
2017-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
8
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Colorado Denver
Department
Pharmacology
Type
Schools of Medicine
DUNS #
City
Aurora
State
CO
Country
United States
Zip Code
80045
Walter, Nicholas D; de Jong, Bouke C; Garcia, Benjamin J et al. (2016) Adaptation of Mycobacterium tuberculosis to Impaired Host Immunity in HIV-Infected Patients. J Infect Dis 214:1205-11
Lipner, Ettie M; Garcia, Benjamin J; Strong, Michael (2016) Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections. PLoS One 11:e0146585
Funk, Christopher S; Cohen, K Bretonnel; Hunter, Lawrence E et al. (2016) Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition. J Biomed Semantics 7:52
Siska, Charlotte; Bowler, Russell; Kechris, Katerina (2016) The discordant method: a novel approach for differential correlation. Bioinformatics 32:690-6
Garcia, Benjamin J; Loxton, Andre G; Dolganov, Gregory M et al. (2016) Sputum is a surrogate for bronchoalveolar lavage for monitoring Mycobacterium tuberculosis transcriptional profiles in TB patients. Tuberculosis (Edinb) 100:89-94
Nickerson, M L; Witte, N; Im, K M et al. (2016) Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response. Oncogene :
Kanigel Winner, Kimberly R; Costello, James C (2016) A SPATIOTEMPORAL MODEL TO SIMULATE CHEMOTHERAPY REGIMENS FOR HETEROGENEOUS BLADDER CANCER METASTASES TO THE LUNG. Pac Symp Biocomput 22:611-622
Vehlow, Corinna; Kao, David P; Bristow, Michael R et al. (2015) Visual analysis of biological data-knowledge networks. BMC Bioinformatics 16:135
Karimpour-Fard, Anis; Epperson, L Elaine; Hunter, Lawrence E (2015) A survey of computational tools for downstream analysis of proteomic and other omic datasets. Hum Genomics 9:28
Hinterberg, Michael A; Kao, David P; Bristow, Michael R et al. (2015) Peax: interactive visual analysis and exploration of complex clinical phenotype and gene expression association. Pac Symp Biocomput :419-30

Showing the most recent 10 out of 70 publications