This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).
This is a preliminary program.
Tuesday, May 29, 2007 Chairs: Donald Henson, Dechang Chen 12:00 - 1:15 Registration and Lunch 1:15 - 1:30 Welcome and Opening Remarks Tami Carpenter, DIMACS Associate Director Dechang Chen, Uniformed Services University 1:30 - 2:00 The Purpose and Objectives of the American Joint Committee on Cancer Stephen Edge, Roswell Park Cancer Institute 2:00 - 2:30 The Development of Adjuvant: A Tool for Estimating Risk of Negative Outcome and the Impact of Adjuvant Therapy Peter Ravdin, MD Anderson Cancer Center 2:30 - 3:00 The Inclusion of Comorbidity in Cancer Statistics Jay Piccirillo, Washington University School of Medicine and Siteman Cancer Center 3:00 - 3:30 Break 3:30 - 4:00 Improving Colon Cancer Staging with Nomograms Martin Weiser, Weill Medical College of Cornell University and Memorial Sloan-Kettering Cancer Center 4:00 - 4:30 The Ideal Staging System Donald Henson, George Washington University 4:30 - 5:30 Open Discussion 5:30 Dinner at DIMACS Wednesday, May 30, 2007 Chairs: William Shannon, Xue-Wen Chen, Li Sheng 8:15 - 9:00 Breakfast and Registration 9:00 - 9:45 Correlating Microarray and Clinical Data with Outcome for Colon Cancer Patients Gunter Schemmann, Princeton University and the University of Medicine and Dentistry of New Jersey 9:45 - 10:30 New Methods for Predicting Outcome in Cancer Patients Dechang Chen, Uniformed Services University 10:30 - 11:00 Break 11:00 - 11:45 Cluster Analysis in Tumor Staging William Shannon, Washington University in St. Louis 12:00 - 1:30 Lunch 1:30 - 2:15 Exponential Decomposition and Computer Simulation of Colorectal Cancer Survival Data David Charkes, Temple University Hospital 2:15 - 3:00 Refining Glioma Subtypes for Identifying Efficient Prediction Signatures Using Gene Expression Profiling Data Aiguo Li, National Cancer Institute 3:00 - 3:30 Conclusions