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
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