The Cancer Informatics Core (CIC) provides services, systems and tools to assist researchers In linking clinical phenotypes and other research data, with primary emphasis on: Clinical Informatics - Clinical informatics services through this core Include the identification, extraction, transformation, and interpretation of clinical data, whether captured as a part of routine care or through research activities. caBIG consulting and deployment - The CIC provides consulting services regarding caBIG technology and tools and also coordinates UMCCC's involvement with the caBIG program (for example, deploying caTISSUE to address issues associated with biospecimen repositories). Bioinformatics - Bioinformatics services include consulting on methodology and appropriate application of technology, a variety of analytical methods, and Integration of analysis from multiple experiments. The majority of the CIC's activities leverage larger IT-service providers in the University of Michigan Health System (UMHS). Although the CIC is small compared with the total UMHS IT staff (the CIC had 3 FTEs in 2010, and will grow to 9 in 2011, compared to more than 800 IT staff across UMHS), its effect is substantially magnified through leveraged Interactions with other, much larger existing resources. Accordingly, the CIC adheres to established and emerging standards and, where possible, uses software developed by national cooperative projects such as caBIG and the CTSAs. Many units in the UMHS offer IT services that can be used by UMCCC researchers. Although the CIC Is, in theory, a facility with a seven-year history, its current configuration is just over one year old.
The Cancer Informatics Core provides highly specialized services, systems and tools to assist cancer researchers with analyzing large data sets used in basic, clinical and translational cancer research.
|Mathewson, Nathan D; Jenq, Robert; Mathew, Anna V et al. (2016) Gut microbiome-derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease. Nat Immunol 17:505-13|
|Owen, John Henry; Graham, Martin P; Chinn, Steven B et al. (2016) Novel method of cell line establishment utilizing fluorescence-activated cell sorting resulting in 6 new head and neck squamous cell carcinoma lines. Head Neck 38 Suppl 1:E459-67|
|Lee, Alice W; Ness, Roberta B; Roman, Lynda D et al. (2016) Association Between Menopausal Estrogen-Only Therapy and Ovarian Carcinoma Risk. Obstet Gynecol 127:828-36|
|Kadakia, Kunal C; Snyder, Claire F; Kidwell, Kelley M et al. (2016) Patient-Reported Outcomes and Early Discontinuation in Aromatase Inhibitor-Treated Postmenopausal Women With Early Stage Breast Cancer. Oncologist 21:539-46|
|Boonstra, Philip S; Mukherjee, Bhramar; Gruber, Stephen B et al. (2016) Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. Am J Epidemiol 183:237-47|
|Peng, Dongjun; Tanikawa, Takashi; Li, Wei et al. (2016) Myeloid-Derived Suppressor Cells Endow Stem-like Qualities to Breast Cancer Cells through IL6/STAT3 and NO/NOTCH Cross-talk Signaling. Cancer Res 76:3156-65|
|Hardiman, Karin M; Ulintz, Peter J; Kuick, Rork D et al. (2016) Intra-tumor genetic heterogeneity in rectal cancer. Lab Invest 96:4-15|
|Boonstra, Philip S; Taylor, Jeremy M G; Smolska-Ciszewska, Beata et al. (2016) Alpha/beta (Î±/Î²) ratio for prostate cancer derived from external beam radiotherapy and brachytherapy boost. Br J Radiol 89:20150957|
|Amin, Nisar A; Malek, Sami N (2016) Gene mutations in chronic lymphocytic leukemia. Semin Oncol 43:215-21|
|Zhao, Ende; Maj, Tomasz; Kryczek, Ilona et al. (2016) Cancer mediates effector T cell dysfunction by targeting microRNAs and EZH2 via glycolysis restriction. Nat Immunol 17:95-103|
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