Applications of Genetic Programming in Cancer Research

1. Applications of Genetic Programming in Cancer Research KooshaTahmasebipour 4V82 Seminar

2. Cancer Research What does cancer research follow? Prevention Diagnosis Treatment Cure

3. Cancer Classification Bioinformatics is applied for cancer research to classify cancers Classification of cancers is a major research area in medical field Classification of cancer is an important step in determining treatment and prognosis When it comes to classification, GP comes out!

4. How cancer occurs? A disease like a cancer is fundamentally a malfunction of genes Utilizing the gene expression data might be the most direct diagnosis approach

5. Gene Expression Level Gene expression can be now easily simulated in the lab via a powerful method named DNA microarray hybridisation. This is a promising tool that generates large-scale gene expression profiles including valuable information Expression level of the gene is the constant numerical value assigned to the expression process of that gene The gene expression value is measured by a technique called northern blot The gene expression level of a gene is proportional to the extent of the activity the gene has in the cell How to interpret gene expression data?

6. Overview of Applying GP for Cancer Research • Objectives: • Classification of cancer into different cancer types based on gene expression patterns. • The identification of the subset of genes that could have profound implications on occurrence of cancer • Why using GP to achieve these objectives? • GP’s unbiased feature selection • Ability to mathematically combine expression levels • Classification rules produced by GP are quite parsimonious and able to capture maximum information from a small number of frames.

7. Overview of Applying GP for Cancer Research How to identify the small subset of genes which plays an important part in diagnosis and treatment of cancer? After running GP on gene expression profiles of cancer patients, only a small set of genes are frequently selected to be used in classifying rules! GP has high tendency in selecting certain genes across classifiers Conclusion: Such genes may be truly important for cancer prognosis and treatment!

8. Sample Research (1) Done by Anriban P Mitra et al [2006]

9. Results Rules gained in each of the 11 folds in a single run

10. Results Testing the best obtained rule on Genes Expressions of testing set

11. Identification of Genes with more implication on bladder cancer • KDR, MAP2K6 and ICMAI are probably the most effective genes in presence of bladder cancer • Next Step: Re-evolve the application using GP considering just 3 most frequently used genes as terminal set.

12. Results Results after running GP on just 3 most effective genes

13. Sample Research (2) Done by Jianjun Yu et al [2007]

14. Results The statistical Z score of each of the 3547 genes occurring in the 1000 classifier generated

15. Best classifier rule IF (HCLS1 − GSTA4 > XPO6) THENBL IF (PTPN13 / COX8A > CDK6) THENEWS IF (SATB1 > CSDA~2) THENNB IF (CDH17 / FGFR4 <= MYL4) THENRMS IF(ARL6IP > MYH11) THENMET

16. Discussion Advantages of using GP in this area There is a strong need to build molecular classifiers made of small number of genes in clinical diagnoses. GP can generate effective classifiers with a small number of genes For each run of GP, tens to hundreds of classifiers can be generated. That provides the ability to compare the frequently used genes in different rules, consequently, more accurate subset of feature genes can be determined. The capability of GP in generating multiple candidate classifiers and combining such classifiers can be led to find better classifier rules compared to the classifiers gained by other machine learning techniques rather than GP

17. Discussion Possible related studies Running GP on gene expression profiles of patient and impatient samples combined together to get more information about feature genes involves with cancers Running GP on gene expression profiles of patient humans combined with gene expression profiles of another species rather than human which apparently have the same tumors inside their cells Replication of this studies for another disease rather than cancer

18. References [1] Jianjun Yu et al. Feature Selection and Molecular Classification of Cancer Using Genetic Programming. Neoplasia, Vol. 9, No. 4, April 2007, pp. 292 – 303 [2] Anirban P Mitra et al. The use of Genetic Programming in the analysis of quantitative gene expression profiles for identification of nodal status in bladder cancer. Biomet Central, BMC cancer, 16 June 2006 [3] Richard J. Gilbert et al. Genomic Computing: explanatory modeling for functional genomics. CiteSeerxβ, 2000 [4] Jin-Hyuk Hong et al. The classificatoin of cancer based on DNA microarray data that uses diverse ensemble genetic programming. Elsevier, Aritificial Intelligence in Medicine, 2005 [5] William P. Worzel et al. Applications of genetic programming in cancer research. Elzevier, Biochemistry & Cell Biology. Oct 2008

19. Questions? Tehran, Iran