Vanderbilt University is positioned to achieve compelling scientific advancement in the areas of genomics and proteomics--combining innovative computational approaches with advances in basic biological science. A steady stream of new quantitative techniques and tools will enable progress in functional biology; this stream depends upon research results from informatics, biomathematics, and computer science. To stay competitive in these new arenas, leading universities must develop new organizational strategies for assembling research teams, keeping investigators current, and providing Core and infrastructure support. Rapid changes in basic science and computational methods, and the multidisciplinary nature of research, mandate such an approach. Vanderbilt proposes a Linked Knowledge Model as an organizational framework for creating an environment of excellence in biomedical information science and technology to maximize multidisciplinary research. Elements of this model: 1) on-going development of trans-institutional strategy; 2) use of the resulting vision to coordinate recruitment and incorporate intellectual capital into flexible problem solving teams; 3) adoption of modular education programs to move new knowledge rapidly across boundaries, serving both neophytes and established investigators; and, 4) creation of centers of excellence combining a traditional support Core with research into next generation techniques, tool development and education. Vanderbilt's BISTI planning project will test and refine the elements of the Linked Knowledge organization model. It will showcase prototypic multidisciplinary scientific projects that produce new informatics and computational research and tools to address challenging biological problems. It will position Vanderbilt to establish a full-scale national program of excellence in biomedical computing. Organizational lessons, tools/techniques, and biologic findings will be broadly applicable beyond Vanderbilt.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Exploratory Grants (P20)
Project #
5P20LM007613-02
Application #
6661965
Study Section
Special Emphasis Panel (ZRG1-SSS-E (01))
Program Officer
Friedman, Charles P
Project Start
2002-09-30
Project End
2005-09-29
Budget Start
2003-09-30
Budget End
2005-09-29
Support Year
2
Fiscal Year
2003
Total Cost
$404,091
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Reif, David M; Moore, Jason H (2006) Visual analysis of statistical results from microarray studies of human breast cancer. Oncol Rep 15 Spec no.:1043-7
Statnikov, Alexander; Aliferis, Constantin F; Tsamardinos, Ioannis et al. (2005) A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21:631-43
Hao, Mingming; Li, Xia; Rizzo, Mark A et al. (2005) Regulation of two insulin granule populations within the reserve pool by distinct calcium sources. J Cell Sci 118:5873-84
Reif, David M; Dudek, Scott M; Shaffer, Christian M et al. (2005) Exploratory visual analysis of pharmacogenomic results. Pac Symp Biocomput :296-307
Statnikov, Alexander; Tsamardinos, Ioannis; Dosbayev, Yerbolat et al. (2005) GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int J Med Inform 74:491-503
Brown, Laura E; Tsamardinos, Ioannis; Aliferis, Constantin F (2004) A novel algorithm for scalable and accurate Bayesian network learning. Medinfo 11:711-5
Statnikov, Alexander; Aliferis, Constantin F; Tsamardinos, Ioannis (2004) Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development. Medinfo 11:813-7
Reif, David M; White, Bill C; Moore, Jason H (2004) Integrated analysis of genetic, genomic and proteomic data. Expert Rev Proteomics 1:67-75
Aliferis, C F; Tsamardinos, I; Statnikov, A (2003) HITON: a novel Markov Blanket algorithm for optimal variable selection. AMIA Annu Symp Proc :21-5