Biomedical informatics and data management are key components of personalized health care. The Department of Biomedical Informatics (BMI) at The Ohio State University Medical Center is playing a critical role in initiatives to translate, integrate, share and analyze information that will improve medical diagnosis, treatment and patient outcomes.
Collaborating with multiple research programs at Ohio State, BMI is a leader in many areas developing cutting-edge technologies, which enable the integration and sharing of data resources. As part of the National Cancer Institute’s Cancer Biomedical Informatics Grid (caBIG™) initiative, BMI developed caGrid, middleware that allows sharing of data and analytical tools among cancer researchers. caGrid has been adopted for imaging, clinical trials, tissue banking and high-throughput molecular studies.
BMI is a leader in imaging informatics, which creates tools, algorithms and technologies to share and analyze biomedical image data. One emphasis is on algorithm development for clinical image analysis and automated classification of different cancers. A second focus is on using image analysis to quantitatively characterize the phenotypic features at molecular, cellular and tissue levels in 2-D and 3-D spaces in breast cancer and the tumor microenvironment.
BMI’s high-end computing group extended its work in combinatorial algorithms and parallel computing. Combinatorial algorithms are an enabling technology for scientific computing, especially for large-scale problems and high performance. These techniques are improving computational performance in pathology image analysis and will be applied to statistical genetics and detection of genetic interactions to predict genetic risk.
Systems biology/bioinformatics group focuses on bioinformatic analysis of gene regulation involving chromatin, transcription actor interactions with DNA, promoter analysis and miRNA. Another line of investigation is the development of computational and evolutionary sciences in a comparative genomics context, including the development of novel phylogenetic methods to correlated genotypes and phenotypes, and to find diagnostic polymorphisms among organisms.
References
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Saltz J, Oster S, Hastings S, Langella S, Kurc T, Sanchez W, Kher M, Manisundaram A, Shanbhag K, Covitz P caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid. Bioinformatics. 2006; 1;22(15):1910-6.
Gurcan MN, Pan T, Sharma A, Kurc T, Oster S, Langella S, Hastings S, Siddiqui KM, Siegel EL, Saltz J GridIMAGE: a novel use of grid computing to support interactive human and computer-assisted detection decision support. J Digit Imaging. 2007;20(2):160-71.
Sharma A, Pan T, Cambazoglu BB, Gurcan M, Kurc T, Saltz J VirtualPACS-A Federating Gateway to Access Remote Image Data Resources over the Grid. J Digit Imaging. 2007 Sep 18.
Mosaliganti K, Janoos F, Sharp R, Ridgway R, Machiraju R, Huang K, Wenzel P, deBruin A, Leone G, Saltz J Detection and visualization of surface-pockets to enable phenotyping studies. IEEE Trans Med Imaging. 2007;26(9):1283-90.
Oster S, Langella S, Hastings S, Ervin D, Madduri R, Phillips J, Kurc T, Siebenlist F, Covitz P, Shanbhag K, Foster I, Saltz J caGrid 1.0: An Enterprise Grid Infrastructure for Biomedical Research. J Am Med Inform Assoc.
To locate more information pertaining to the above referenced articles please visit the PubMed database.