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MEME KANSERİ SINIFLANDIRMASI İÇİN VERİ FÜZYONU VE GENETİK ALGORİTMA TABANLI GEN SEÇİMİ

GENE SELECTION FOR BREAST CANCER CLASSIFICATION BASED ON DATA FUSION AND GENETIC ALGORITHM

Journal Name:

  • Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

Publication Year:

  • 2012

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Early diagnosis of breast cancer has been playing very important role on treatment of the disease. In this work, a new feature selection method for breast cancer classification based on data fusion and genetic algorithm is presented. The study consists of two steps: In the first step, the dimensionality of the gene expression dataset was reduced with filter method and the second step, significant genes have been identified with genetic algorithm. SVM was used for fitness function in genetic programming. In this study the classification accuracy rate was obtained 94.65 % when using selected 10 genes.
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Abstract (Original Language): 
Ciddi rahatsızlıklardan biri olan meme kanserinin erken teşhis edilmesi hastalığın tedavisinde önemli rol oynar. Bu çalışmada meme kanseri hastalığında etkin rol oynayan genlerin belirlenmesi için veri füzyonu ve genetik algoritma tabanlı yeni bir nitelik seçme metodu önerilmektedir. Yapılan çalışma iki aşamadan oluşmaktadır: İlk aşamada filtreleme yöntemi ile gen ifade verisi indirgenmiş, ikinci aşamada genetik algoritma ile meme kanserinde etkin rol alan genlerin tespiti gerçekleştirilmiştir. Destek vektör makinesi, genetik algoritma için uygunluk fonksiyonu olarak kullanılmıştır. Yapılan çalışmada belirlenen 10 gen ile sınıflandırma doğruluk oranı %94,65 elde edilmiştir.
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  • Turkish

REFERENCES

References: 

1. Segal, E.,Wang, H. ve Koller, D., “Discovering Molecular Pathways From Protein Interaction And Gene Expression Data”, Bioinformatics, Cilt 19, 264-272, 2003.
2. Hedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O.P., Wilfond, B., Borg, A., Trent, J., “Gene-Expression Profiles in Hereditary Breast Cancer”, The New England Journal of Medicine, Cilt 344, 539-548, 2001.
3. van de Vijver, M.J, He, Y.D., van't Veer, L.J., Dai, H., Hart, A.A., Voskuil, D.W., Schreiber, G.J., Peterse, J.L., Roberts, C., Marton, M.J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E.T., Friend, S.H., Bernards, R., “A Gene-Expression Signature As A Predictor Of Survival In Breast Cancer”, The
New England Journal of Medicine, Cilt 347, 1999-2009, 2002.
4. Peng, Y., Wu, Z., Jiang, J., “A Novel Feature Selection Approach For Biomedical Data Classification”, Journal of Biomedical Informatics, Cilt 43, 15-23, 2010.
5. Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D. ve Levine, A. J., “Broad Patterns Of Gene Expression Revealed By Clustering Analysis Of Tumor And Normal Colon Tissues Probed By Oligonucleotide Arrays”, Cell Biology, Cilt 96, 6745-6750, 1999.
6. Dudoit, S., Fridlyand, J ve Speed, T., P., “Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data”, American Statistical Association, Cilt 97, 77-87, 2002.
7. Horng, J.T., Wub, L.C., Liu, B.J., Kuo, J.L., Kuo, W.H., Zhang, J.J., "An Expert System To Classify Microarray Gene Expression Data Using Gene Selection By Decision Tree", Expert Systems with Applications, Cilt 36, 9072-9081, 2009.
8. Wang, Y., Tetko, I.V., Hall, M.A., Frank, E., Facius, A., Mayer, K., Mewes, H., “Gene Selection From Microarray Data For Cancer Classification-A Machine Learning Approach”, Computational Biology and Chemistry, Cilt 29, 37-46, 2005.
9. Uriarte, R., D. ve Andrés, S.,A., “Gene Selection And Classification Of Microarray Data Using Random Forest”, BMC Bioinformatics, Cilt 7, 1-13, 2006.
10. Li, L., Jiang, W., Li, X., Moser, K.L., Guo, Z., Du, L., Wang, Q., Topol, E., Wang, Q., Rao, S., “A Robust Hybrid Between Genetic Algorithm And Support Vector Machine For Extracting An Optimal Feature Gene Subset”, Genomics, Cilt 85, 16-23, 2005.
11. Antonov, A.V., Tetko, I.V., Kosykh, D., Surmeli, D., Mewes, H.W., “Exploiting Scale-Free Information From Expression Data For Cancer Classification”, Computational Biology and Chemistry, Cilt 29, 288–293, 2005.
12. Saeys, Y., Inza, I. ve Larranaga, P., “A Review Of Feature Selection Techniques In Bioinformatics”, Bioınformatics, Cilt 23, 2507–2517, 2007.
13. Maldonado, S., Weber, R., “A Wrapper Method For Feature Selection Using Support Vector Machines”, Information Sciences, Cilt 179, 2208–2217, 2009.
14. Huang, C., L. Ve Wang, C., J., “A GA-Based Feature Selection And Parameters Optimization For Support Vector Machines”, Expert Systems with Applications, Cilt 31, 231–240, 2006.
15. Liu, H. ve Motoda, H., “Computational Methods Of Feature Selection”, Chapman & Hall/CRC, Taylor & Francis Group 6000 Broken Sound Parkway NW, 26-27, 2008.
Gazi Üniv. Müh. Mim. Fak. Der. Cilt 27, No 3, 2012 667
O.Yıldız ve ark. Meme Kanseri Sınıflandırması için Veri Füzyonu ve Genetik Algoritma Tabanlı Gen Seçimi
16. Inza, I., Larranaga, P., Blanco, R., Cerrolaza, A.J., “Filter Versus Wrapper Gene Selection Approaches In DNA Microarray Domains”, Artificial Intelligence in Medicine, Cilt 31, 91-103, 2004.
17. Goldberg, E.D., “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison-Wesley Longman, Inc., New York, 1989.
18. Michalewicz, Z., “Genetic Algorithms + Data Structures = Evolution Programs”, Springer, Berlin, 1992.
19. Şen, Z., ”Genetik Algoritma Ve En İyileme Yöntemleri”, Su Vakfı Yayınları, 2004
20. Lee, C., P., Lin, W., S., Chen, Y., M. ve Kuo B., J., ”Gene Selection And Sample Classification On Microarray Data Based On Adaptive Genetic Algorithm/K-Nearest Neighbor Method”, Expert Systems with Applications, Cilt 38, 4661–4667, 2011.
21. Li, L., Weinberg, C., R., Darden, T.A. ve Pedersen, L.G., “Gene Selection For Sample Classification Based On Gene Expression Data: Study Of Sensitivity To Choice Of Parameters Of The GA/KNN Method”, Bioinformatics, Cilt. 17, 1131–1142, 2001.
22. Hernandez, J.,C., Duval, D., ve Hao, J., K., “A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data”, EvoBIO'07 Proceedings of the 5th European
conference on Evolutionary computation, machine learning and data mining in bioinformatics, Springer-Verlag Berlin, Heidelberg 2007.
23. Lee, C., P. ve Leu, Y., “A Novel Hybrid Feature Selection Method For Microarray Data Analysis”, Applied Soft Computing, Cilt 11, 208–213, 2011.
24. Özkan, Y., “Veri Madenciliği Yöntemleri”, Papatya Yayıncılık, İstanbul, 2008.
25. Cristianini, N. ve Taylor, J.S., “An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods”, Cambridge Univ. Press, 2000.
26. Lasko, T.,A., Bhagwat, J., G., Zou, K., H. ve Ohno-Machado, L., “The Use Of Receiver Operating Characteristic Curves In Biomedical Informatics”, Journal of Biomedical Informatics, Cilt 38, 404–415, 2005.
27. http://bioinformatics.nki.nl/data.php
28. Veer,L.J,V.,Dai,H., Vijver, M.J.V., He, Y.D., Hart, A. A. M., Mao, M., Peterse, H.L., Kooy, K.V.D., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H., “Gene Expression Profiling Predicts Clinical Outcome Of Breast Cancer”, Nature, Cilt 415, 530-536, 2002.

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