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Team scores at bioinformatics competition

Two MUSC teams, composed of system biologists, were recognized in the 4th Dialogue on Reverse Engineering Assessment and Methods (DREAM) workshop held in November.
MUSC teams included John H. Schwacke, Ph.D., assistant professor in the Department of Biochemistry and Molecular Biology; Jim W. Zheng, Ph.D., assistant professor, Division of Biostatistics and Epidemiology;  Xinghua Lu, M.D, Ph.D., associate professor, Division of Biostatistics and Epidemiology; Kellie J. Sims, Ph.D.,  Lam Tsoi (Alex) and Tingting Qin, both graduate students in the Division of Biostatistics and Epidemiology.
Schwacke tied for first place and was designated with a “Best Performer” award. His team also was invited to present Dec. 4 at the RECOMB and DREAM4 Reverse Engineering Challenge at the Massachusetts Institute of Technology’s Broad Institute in Boston. A second team led by Zheng, placed third after a tie for first among two other teams.
The event is part of an annual international bioinformatics competition for system biologists and researchers to use computational methods to solve five biological challenges to make predictions about networks from provided data. Teams focused on developing and assessing reverse engineering methods applied to problems such as gene regulatory network inference, binding domain prediction and signal transduction pathway prediction.
The annual competition is devised from both simulated and experimental data donated by leading system biology research groups. It’s organized and sponsored by Columbia University, IBM’s Computational Biology Center, the NIH Roadmap Initiative and New York Academy of Sciences.
MUSC’s bioinformatics research group within the Department of Biochemistry and Molecular Biology has made significant contributions to the research development at MUSC, particularly to the research infrastructure including the COBRE in Lipidomics and Biopathology, the NHLBI Proteomics Center, and SCTR. The faculty  members engage in a variety of bioinformatics and computational biology research efforts including signal transduction system inference and modeling, proteomic, lipidomic and transcriptomic data analysis, metabolic pathway modeling, functional genomics, and text mining.
For information on the group’s research interests and capabilities, contact Lu at

Friday, Dec. 11, 2009

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