The University of Missouri Biomedical and Health Informatics Research Training (BHIRT) Program prepares exceptionally qualified individuals for academic careers in medical informatics across a wide range of disciplines in the life sciences and information sciences, including medicine, nursing, veterinary medicine, bioinformatics, medical librarianship and health services research. The program, which has been a leader in medical informatics since the 1960's and has trained over 75 fellows, is unique because of its cross-disciplinary approach and collaborative research opportunities. The BHIRT Program, directed by Dr. Joseph W. Hales, Director of Health Informatics Programs and of the D. A. B. Lindberg Center, has eight core faculty, plus twenty-two affiliated faculty experienced in informatics and specific relevant research areas. An additional 70 faculty participate who have been active in medical informatics research. The BHIRT Program has 14 pre- and postdoctoral trainees. Special efforts are made to recruit applicants from under-represented groups. The curriculum emphasizes three research foci: bioinformatics, e-Health, and strategic management of health information. Because these three foci fit well with the institutional strategic focus on bioinformatics, and with ongoing IAIMS and telemedicine activities, there is exceptional opportunity for significant, innovative and much needed research. Trainees are assigned to one or more senior researchers and are evaluated regularly. Trainees satisfy prerequisites, take advanced core courses, complete a rotation in applications of informatics, present and publish research papers, participate in symposia, and are trained in principles of responsible conduct of research.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
5T15LM007089-14
Application #
6915174
Study Section
Special Emphasis Panel (ZLM1-MMR-T (J2))
Program Officer
Florance, Valerie
Project Start
1997-07-01
Project End
2007-06-30
Budget Start
2005-07-01
Budget End
2006-06-30
Support Year
14
Fiscal Year
2005
Total Cost
$381,484
Indirect Cost
Name
University of Missouri-Columbia
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211
Grinter, Sam Z; Zou, Xiaoqin (2014) A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions. J Comput Chem 35:932-43
Bryan, Jeffrey N; Kumar, Senthil R; Jia, Fang et al. (2014) Zebularine significantly sensitises MEC1 cells to external irradiation and radiopharmaceutical therapy when administered sequentially in vitro. Cell Biol Int 38:187-97
Li, Jilong; Deng, Xin; Eickholt, Jesse et al. (2013) Designing and benchmarking the MULTICOM protein structure prediction system. BMC Struct Biol 13:2
Green, Jason M; Paladugu, Sowjanya; Shuyu, Xu et al. (2013) Using temporal mining to examine the development of lymphedema in breast cancer survivors. Nurs Res 62:122-9
Huang, Sheng-You; Yan, Chengfei; Grinter, Sam Z et al. (2013) Inclusion of the orientational entropic effect and low-resolution experimental information for protein-protein docking in Critical Assessment of PRedicted Interactions (CAPRI). Proteins 81:2183-91
Grinter, Sam Z; Yan, Chengfei; Huang, Sheng-You et al. (2013) Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark. J Chem Inf Model 53:1905-14
Perez-Bercoff, Asa; Hudson, Corey M; Conant, Gavin C (2013) A conserved mammalian protein interaction network. PLoS One 8:e52581
Eickholt, Jesse; Cheng, Jianlin (2013) A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. BMC Bioinformatics 14 Suppl 14:S12
Eickholt, Jesse; Cheng, Jianlin (2013) DNdisorder: predicting protein disorder using boosting and deep networks. BMC Bioinformatics 14:88
Tegge, Allison N; Caldwell, Charles W; Xu, Dong (2012) Pathway correlation profile of gene-gene co-expression for identifying pathway perturbation. PLoS One 7:e52127

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