Biomedical informatics spans a research space essential in a number of areas of life sciences and healthcare. The demand for well-trained research scientists in this area is exploding, although there is real need for independent research and informatics tool development, there is also an increasing emphasis on interdisciplinary research, where biomedical informaticians play a central role. We propose a combined predoctoral (6 positions) and post-doctoral (6 positions) research training program that prepares research fellows to become leaders in the academic and research biomedical informatics community and valuable contributors to interdisciplinary biomedical research problems. The goal of the biomedical informatics research training (BIRT) program at the University of Missouri- Columbia (MU) is to provide strong foundational training in informatics theory and methodology, build upon the fundamentals of biological and computational sciences, and prepare research fellows to assume leadership roles working on important problems in life scienes, healthcare, bioinformatics, and computational biology. MU has a strong history of biomedical informatics research training, but with implementaion of a recent planned expansion of informatics resources, our campus research community is now better positioned than ever before to be leaders in biomedical informatics research training and further strengthen life sciences and healthcare research. The creation of the MU Informatics Institute (MUM) brings together biomedical informatics researchers from the campus, fully integrates faculty resources of 2 strong departments, Computer Science and Health Management and Informatics, and provides a structure for leveraging outstanding resources. The MUII will manage the PhD in Informatics program, as well as multidiscipliriary informatics research faculty and research trainees.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
5T15LM007089-20
Application #
8091312
Study Section
Special Emphasis Panel (ZLM1-AP-T (O1))
Program Officer
Florance, Valerie
Project Start
1997-07-01
Project End
2013-06-30
Budget Start
2011-07-01
Budget End
2013-06-30
Support Year
20
Fiscal Year
2011
Total Cost
$151,980
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
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
Li, Jilong; Deng, Xin; Eickholt, Jesse et al. (2013) Designing and benchmarking the MULTICOM protein structure prediction system. BMC Struct Biol 13:2
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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