The United States and other developed countries are experiencing a severe shortage of professionals trained in biological and medical informatics. To retain a leading position at the forefront of modern biology, medicine, pharmaceutical sciences and agriculture, this shortage must be alleviated through the interdisciplinary training of pre-doctoral and post-doctoral fellows. The trainees must be educated in the development and application of mathematical and computational tools that will later enable them effectively to exploit the unprecedented opportunities, and tackle the novel challenges, brought forth by advances in genome technology and molecular biology. To this end, the Department of Biometry and Epidemiology at the Medical University of South Carolina requests funds for an educational program dedicated to the Training of Toolmakers in BioMedical Informatics (TTBMI). This program will educate graduate students and post-doctoral fellows in one focused niche of biomedical information for which MUSC is particularly well equipped. This niche comprises the development and application of methods of computational systems science for connecting genes with their biochemical, physiological and clinical functions. The Department has an established infrastructure and has offered curricula in biostatistics and biomedical systems science since the early 1970's. In 2000, the Department obtained approval from the State of South Carolina to offer a graduate program in biomedical informatics. The requested funds will be used to implement this effort. The proposed program consists of classroom teaching in informatics methods and relevant biological topics, which is subsequently enriched and complemented with hands-on, specific informatics research in one of the numerous participating programs that already exist on campus. Individuals from these programs have expressed enthusiastic support for the proposed program and, in particular, for a mechanism of co-mentoring trainees with faculty from the Department of Biometry and Epidemiology. Through the dual mechanism of informatics training and real world application, graduates will be educated to become toolmakers capable of solving problems in biology and medicine of the 21st century.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
5T15LM007438-03
Application #
6782652
Study Section
Special Emphasis Panel (ZLM1-MMR-T (J2))
Program Officer
Florance, Valerie
Project Start
2002-07-01
Project End
2007-06-30
Budget Start
2004-07-01
Budget End
2005-06-30
Support Year
3
Fiscal Year
2004
Total Cost
$490,967
Indirect Cost
Name
Medical University of South Carolina
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
183710748
City
Charleston
State
SC
Country
United States
Zip Code
29425
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Qin, Tingting; Tsoi, Lam C; Sims, Kellie J et al. (2012) Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network. BMC Syst Biol 6 Suppl 3:S3
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Tsoi, Lam C; Qin, Tingting; Slate, Elizabeth H et al. (2011) Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior. BMC Bioinformatics 12:438
Asbury, Thomas M; Mitman, Matt; Tang, Jijun et al. (2010) Genome3D: a viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome. BMC Bioinformatics 11:444
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Robinson, Christopher J; Hill, Elizabeth G; Alanis, Mark C et al. (2010) Examining the effect of maternal obesity on outcome of labor induction in patients with preeclampsia. Hypertens Pregnancy 29:446-56
Cowart, L Ashley; Shotwell, Matthew; Worley, Mitchell L et al. (2010) Revealing a signaling role of phytosphingosine-1-phosphate in yeast. Mol Syst Biol 6:349
Tsoi, Lam C; Patel, Ravi; Zhao, Wenle et al. (2009) Text-mining approach to evaluate terms for ontology development. J Biomed Inform 42:824-30
Karpievitch, Yuliya V; Hill, Elizabeth G; Leclerc, Anthony P et al. (2009) An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++. PLoS One 4:e7087

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