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CAMDA 2003 Conference Submitted Abstracts

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Wednesday, November 12, 2003

Preconference seminar: John Quackenbush
The Institute for Genome Research
Tutorial software: Java 1.4.1, Java 3D 1.3.1, MIDAS, MeV

Thursday, November 13, 2003

Keynote speaker: David Beer
University of Michigan School of Medicine
Introduction of datasets: Igor Jurisica
University of Toronto
Identification of Prognostic Genes, Combining Information Across Different Institutions and Oligonucleotide Arrays
Morris, Jeffrey, Yin G., Baggerly, K., Wu, C., Zhang, L.
University of Texas MD Anderson Cancer Center
Houston, TX
Application of Survival and Multivariate Methods to Gene Expression Data Combined from Two Sources
Robb, Linda, Stephens, R., Coleman, J.
GlaxoSmithKline, Statistical Sciences UK
Stevenage, UK
The Induction and Analysis of Gene Networks
Livingston, Gary, Li, X., Li, G., Hao, L.
University of Massachusetts - Lowell
Lowell, MA
Bayesian Decomposition Classification Analysis of the CAMDA2003 Data Set
Kossenkov, Andrew, Bidaut, G., Ochs, M.
Fox Chase Cancer Center
Philadelphia, PA
Associating Microarray Data with a Survival Endpoint
Jung, Sin-Ho, Owzar, K., George, S.
Department of Biostatistics and Bioinformatics
Duke Comprehensive Cancer Center
Durham, NC
Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetrix Platform Experiments
Lin, Xiwu, Park, D., Eslava, S., Lee, K., Lam, R., Zhu, L.
GlaxoSmithKline Pharmaceuticals R&D
Collegeville, PA
Weakest Link Models for Detecting Small Groups of Genes to Predict Lung Cancer Survival
Richards, Thomas, Day, R., Kaminski, N.
Simmons Center for Interstitial Lung Diseases
Pittsburgh, PA
Differential Correlation Detects Complex Associations Between Gene Expression and Clinical Outcomes in Lung Adenocarcinomas
Shedden, Kerby, Taylor, J.
Department of Statistics, University of Michigan
Ann Arbor, MI

Friday, November 14, 2003

Keynote speaker: Kenneth Beutow
NCI Center for Bioinformatics
Use of Microarray Data via Model-Based Classification in the Study and Prediction of Survival from Lung Cancer
Jones, L., Ng, S., Ambroise, C., McLachlan, Geoff
Department of Mathematics
University of Queensland
Brisbane, Australia
Microarray Data Integration and Machine Learning Techniques for Lung Cancer Survival Prediction
Berrar, Daniel, Sturgeon, B., Bradbury, I., Dubitzky, W.
School of Biomedical Sciences
University of Ulster at Coleraine
Northern Ireland
Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering
Zhou, W., Wu, W., Palmer, N., Mower, E., Daniels, N., Cowen, L., Blumer, Anselm
Computer Science
Tufts University
Medford, MA
Maximum Expected Utility Modeling of the Conditional Probability of Adenocarcinoma
Friedman, Craig, Cao, W.
Courant Institute, NYU
New York, NY
Neural Network Classifiers and Gene Selection Methods for Microarray Data on Human Lung Adenocarcinoma
Zheng, Gaolin, George, E.O., Narasimhan, G.
School of Computer Science
Florida International University
Miami, FL
Higher Dimensional Approach for Classification of Lung Cancer Microarray Data
Crimins, F., Dimitri, R., Klein, T., Palmer, Nathan, Cowen, L.
Department of Computer Science
Tufts University
Medford, MA
A Combinatorial Approach to the Analysis of Differential Gene Expression Data: The Use of Graph Algorithms for Disease Prediction and Screening
Langston, M., Lin, L., Peng, X., Baldwin, Nicole, Symons, C., Zhang, B.
University of Tennessee
Knoxville, TN
Entropy and Survival-based Weights to Combine Affymetrix Array Types in the Analysis of Differential Expression and Survival
Hu, Jianhua, Yin, G., Morris, J., Zhang, L., Wright, F.
Department of Biostatistics
University of North Carolina
Chapel Hill, NC
Closing remarks: Michael Ochs
Fox Chase Cancer Center

North Carolina Biotechnology Center GlaxoSmithKline Duke Center for Bioinformatics and Computational Biology The Scientist

Last modified on 01/22/2004 11:35:24

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