Genomic Signal Processing: Ensemble Dependence Model for Classification and Prediction of Cancer Based on Gene Expression Data Joseph DePasquale Engineering Frontiers 26 Apr 07

Overview Motivation Background –Genes, Cancer, DNA Microarrays Ensemble Dependence Model –Basic structure –Inclusion in a classification system Results Conclusions

Motivation Estimated 1.4 million new cases of cancer –Roughly 550,000 will die from their disease In New Jersey 43,910 new cases –17,720 deaths In 2005, NIH estimates that the overall cost for cancer → 210 billion dollars

Background What is cancer? –Uncontrolled division of damaged cells Apoptosis –Risk increases with age Cause of unregulated cell growth

Background What is a gene? –Components –Functionality What is the importance of protein? –Essential to all living things –Participate in all functions within cells What is the significance of gene products?

DNA Microarrays Expression profiling –Represents the simultaneous activity of thousands of individual genes Publicly available data –Complexity has led to a need for the standardization of experimental setup MIAME MAQC

Taken from:

Ensemble Dependence Model Genes with similar expression profiles are combined together into clusters –Expression profile of each cluster is the average profile of all genes in that cluster Taken from: P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan

Ensemble Dependence Model

Model-driven method –Feature selection Not all genes are relevant T-test –Gene clustering Number of clusters Gaussian mixture model –Model learning/classification Dependence matrices generated for two cases

Classification Maximum likelihood rule –Binary hypothesis-testing problem –Tests fit of unknown samples to each model Normal Case: Cancer Case:

EDM-Based Cancer Classification Taken from: P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan

Results Taken from: P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan

Results Here, 200 different subsets of gastric data are used to calculate 200 different dependence matrices, eigenvalues of these matrices are plotted Taken from: P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan

Results Eigenvalues = {1, 1, 1, -3}

Results Taken from: P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan

In Summary Taken from: P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan

Conclusions EDM is a model-based system that is used for cancer classification and prediction based on publicly available gene expression data –Dependence of clusters to other clusters Classification results are comparable with widely accepted ML algorithm Eigenvalues of dependence matrix could be a valuable cancer prediction tool

References [1] P. Qui, Z. J. Wang, and K.J.R. Liu. “Genomic Processing for Cancer Classification and Prediction,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp , Jan [2] P. Qui, Z. J. Wang, and K.J.R. Liu. “Ensemble dependence model for classification and prediction of cancer and normal gene expression data,” Bioinformatics, vol. 21, no. 14, pp , May [3] D. Anastassiou. “Genomic Signal Processing,” IEEE Signal Processing Magazine, vol. 18, no. 4, pp. 8-20, July [4] J. Astola, I. Tabus, I. Shmelevich, and, E. Dougherty. “Genomic Signal Processing,” Signal Processing (Elsevier), vol. 83, pp , [5] American Cancer Society. “Cancer Facts and Figures 2006,” ACS :: Statistics for 2006 [Online]. Available: [6] [7] [8] [9] [10] M. Karnick. “Genomic Signal Processing,” Engineering Frontiers, The presentation directly previous to mine, Apr 2007.