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

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

3. 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

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

5. 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?

6. 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

7. Taken from: http://en.wikipedia.org/wiki/DNA_microarray

8. 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. 100-110, Jan. 2007.

9. Ensemble Dependence Model

10. 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

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

12. 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. 100-110, Jan. 2007.

13. 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. 100-110, Jan. 2007.

14. 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. 100-110, Jan. 2007.

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

16. 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. 100-110, Jan. 2007.

17. 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. 100-110, Jan. 2007.

18. 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

19. 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. 100-110, Jan. 2007. [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. 3114-3121, May 2005. [3] D. Anastassiou. “Genomic Signal Processing,” IEEE Signal Processing Magazine, vol. 18, no. 4, pp. 8-20, July 2001. [4] J. Astola, I. Tabus, I. Shmelevich, and, E. Dougherty. “Genomic Signal Processing,” Signal Processing (Elsevier), vol. 83, pp. 691-694, 2003. [5] American Cancer Society. “Cancer Facts and Figures 2006,” ACS :: Statistics for 2006 [Online]. Available: http://www.cancer.org/downloads/STT/CAFF2006PWSecured.pdfhttp://www.cancer.org/downloads/STT/CAFF2006PWSecured.pdf [6] http://en.wikipedia.org/wiki/Gene [7] http://en.wikipedia.org/wiki/Gene_expression [8] http://en.wikipedia.org/wiki/Protein [9] http://en.wikipedia.org/wiki/DNA_microarray [10] M. Karnick. “Genomic Signal Processing,” Engineering Frontiers, The presentation directly previous to mine, Apr 2007.