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 15-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 the biomedical and the 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 in our situation. We are justifiably proud of our outstanding track record as well as of 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. Based on our successful track record, we are requesting that our current slot allocation be continued, that is, 8 predoctoral, 7 postdoctoral and 4 short term positions.

Public Health Relevance

TheColoradoBiomedicalInformaticsTrainingProgramisanindependent,Ph.D.-? grantingandpostdoctoraltrainingprogrambasedintheUniversityofColorado SchoolofMedicine,witha15-?yeartrackrecordofinnovativeandeffectivetraining ofpre-?andpost-?doctoralfellowsforresearchcareers.Weareasecond-?generation teachingprogram,informedbytheexperienceofthemanybiomedicalinformatics trainingmodelsthathavecomebeforeus.Ourprogramisdesignedtoproduce graduateswithdepthinbothcomputationalmethodsandbiomedicine,anintimate familiaritywiththescienceandtechnologythatsynergizesthetwo,andtheskills necessarytopioneernovelcomputationalapproachestosignificantbiomedical questions.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
3T15LM009451-12S1
Application #
9743543
Study Section
Program Officer
Florance, Valerie
Project Start
2007-07-01
Project End
2022-06-30
Budget Start
2018-09-01
Budget End
2019-06-30
Support Year
12
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Colorado Denver
Department
Pharmacology
Type
Schools of Medicine
DUNS #
041096314
City
Aurora
State
CO
Country
United States
Zip Code
80045
Tripodi, Ignacio J; Allen, Mary A; Dowell, Robin D (2018) Detecting Differential Transcription Factor Activity from ATAC-Seq Data. Molecules 23:
Ross, Brian C; Boguslav, Mayla; Weeks, Holly et al. (2018) Simulating heterogeneous populations using Boolean models. BMC Syst Biol 12:64
Russell, Pamela H; Vestal, Brian; Shi, Wen et al. (2018) miR-MaGiC improves quantification accuracy for small RNA-seq. BMC Res Notes 11:296
Rudra, Pratyaydipta; Shi, Wen J; Russell, Pamela et al. (2018) Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse. BMC Genomics 19:639
Callahan, Tiffany J; Baumgartner, William A; Bada, Michael et al. (2018) OWL-NETS: Transforming OWL Representations for Improved Network Inference. Pac Symp Biocomput 23:133-144
Powers, Rani K; Goodspeed, Andrew; Pielke-Lombardo, Harrison et al. (2018) GSEA-InContext: identifying novel and common patterns in expression experiments. Bioinformatics 34:i555-i564
Boguslav, Mayla; Cohen, K Bretonnel; Baumgartner, William A et al. (2018) Improving precision in concept normalization. Pac Symp Biocomput 23:566-577
Lipner, Ettie M; Knox, David; French, Joshua et al. (2017) A Geospatial Epidemiologic Analysis of Nontuberculous Mycobacterial Infection: An Ecological Study in Colorado. Ann Am Thorac Soc 14:1523-1532
Azofeifa, Joseph G; Allen, Mary A; Lladser, Manuel E et al. (2017) An Annotation Agnostic Algorithm for Detecting Nascent RNA Transcripts in GRO-Seq. IEEE/ACM Trans Comput Biol Bioinform 14:1070-1081
Rudra, Pratyaydipta; Shi, W Jenny; Vestal, Brian et al. (2017) Model based heritability scores for high-throughput sequencing data. BMC Bioinformatics 18:143

Showing the most recent 10 out of 86 publications