Lung cancer classification using genetic algorithm to optimize prediction models

IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications

Volumn , Issue , 2014, Pages

  Diaz, Joey Mark ^a   Pinon, Raymond Christopher ^a   Solano, Geoffrey ^a  

^a UNIVERSITY OF THE PHILIPPINES MANILA   (Philippines)

Author keywords

artificial neural networks; feature selection; GALGO; genetic algorithm; microarray; support vector machine

Indexed keywords

ALGORITHMS; BIOLOGICAL ORGANS; DIAGNOSIS; FEATURE EXTRACTION; GENES; GENETIC ALGORITHMS; MICROARRAYS; NEURAL NETWORKS; PATIENT TREATMENT; SUPPORT VECTOR MACHINES;

CANCER RESEARCH; EARLY DIAGNOSIS; GALGO; LUNG CANCER; MEDICAL PROFESSIONALS; MICROARRAY TECHNOLOGIES; PREDICTION MODEL; PREDICTIVE PERFORMANCE;

DISEASES;

EID: 84906766575     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IISA.2014.6878770     Document Type: Conference Paper

References (30)

1. World Cancer Research Fund International. "Lung Cancer". http://www.wcrf.org./cancerstatistics/dataspecificcancers/lung-cancer- statistics.php#BOTH. Accessed: March 2014
2. T. Golub, D. Slomin, P. Tamayo, C. Huard, et al., "Molecular Classification of Cancer: Class Discovery and Prediction by Gene Expression Monitoring" in Science, Vol. 286, October 1999, pp. 531-537.
3. A. Alizadeh, M. Eisen, R. Davis, C. Ma, et al., "Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling" in Nature 403, February 2000, pp. 503-511.
4. Y. Lu and J. Han, "Cancer Classification Using Gene Expression Data" in Information Systems (Special Issue on Data Management in Bioinformatics) 28, 2003, pp. 243-268.
5. E. Tang, P. Suganthan and X. Yao, "Feature Selection for Microarray Data Using Least Squares SVM and Particle Swarm Optimization", Computational Intelligence in Bioinformatics and Computational Biology 2005. [Proceedings of the 2005 IEEE Symposium, November 2005].
6. D. Dasgupta, "Artificial Neural Networks and Artificial Immune Systems: Similarities and Differences", Proceedings of the IEEE Systems, Man, and Cybernetics, vol. 1, 1997, pp. 873-878.
7. V. Bevilacqua, G. Mastronardi, F. Menolascina, A. Paradiso and S. Tommasi, "Genetic Algorithms and Artificial Neural Networks in Microarray Data Analysis: a Distributed Approach", Engineering Letters, vol. 13, no. 3, 2006, pp. 335-343.
8. V. Trevino and F. Falciani, "GALGO: an R package for multivariate variable selection using genetic algorithms", in Bioinforratics Oxford Journals Vol. 22, no. 9, 2006, pp. 1154-1156.
9. C. Ooi and P. Tan, "Genetic algorithms applied to multi-class prediction for the analysis of gene expression data", in Bioinforratics Oxford Journals Vol. 19, no. 1, 2003, pp. 37-44.
10. Z. Zhu, Y. Ong and M. Dash, "Markow Blanket-Embedded Genetic Algorithm for Gene Selection", in Pattern Recognition Vol. 40, issue 11, November 2007, pp. 3236-3248.
11. Y. Saeys, I. Inza and P. Larranaga, "A Review of Feature Selection Techniques in Bioinformatics", in Bioinformatics Oxford Journals Vol. 23, Issue 19, 2007, pp. 2507-2517.
12. W. Daelemans, et al., "Combined optimization of feature selection and algorithm parameter interaction in machine learning of language", in Proceedings of the 14th European Conference on Machine Learning (ECML-2003), Lecture Notes in Computer Science Vol. 2837, 2003, pp. 84-95.
13. J. Guyon and A. Elisseeff, "An introduction to variable and feature selection", in The Journal of Machine Learning Research Vol. 3, Issue 3, 2003, pp. 1157-1182.
14. H. Liu and H. Motoda, "Feature Selection for Knowledge Discovery and Data Mining", in The Springer International Series in Engineering and Computer Science Vol. 454, 1998.
15. J. Yu, S. Ongarello, R. Fiedler, X. Chen, G. Toffolo, C. Cobelli, et al., "Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data", in Bioinformatics Oxford Journals, Vol. 21, Issue 10, pp. 2200-2209.
16. T. Lal, O. Chapelle, J. Weston and A. Elisseeff, "Embedded Methods", in Feature Extraction Studies in Fuzziness and Soft Computing, Vol. 207, 2006, pp. 137-165.
17. W. Krijnen, Chapter 8: Classification Methods in Applied Bioinformatics using R, 2009, pp. 161-163.
18. B. Ripley, Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press, 1996.
19. A. Bhattacharjee, W. Richards, J. Staunton, C. Li, S. Monti, P. Vasa, et al., "Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses", PNAS, Vol. 98, no. 24, 2001, pp. 13790-13795.
20. V. Trevino and F. Falciani, "Galgo: Genetic Algorithms for Multivariate Statistical Models from Large-scale Functional Genomics Data", R package version 1.2, 2014. http://bioinformatica.mty.itesm.mx/?q=node/82
21. P. Jha, M. Kent Ranson, S. Nguyen, D. Yach, "Estimates of global and regional smoking prevalence in 1995 by age and sex". American Journal of Public Health, 92(6), pp. 1 002-1 006. 2002.
22. GLOBOCAN 2000. Cancer incidence, mortality and prevalence worldwide. IARC Cancer Base NO. 5. Version 1.0.1 [online database]. Lyon, International Agency for Research on Cancer, 2001.
23. Ramaswamy, S., Tamayo, P., Ritkin, R., Mukherjee, S., Yeang, C., Angelo, M., et al., "Multiclass cancer diagnosis using tumor gene expression signatures", Proc Natl Acad Sci USA, 98(26), 15149-15154, 2001.
24. Stein, L., D., "Human genome: End of the beginning", Nature, 431, 915-916, 2004.
25. R. Tibshirani, T. Hastie, B. Narasimhan and G. Chu, "Diagnosis of multiple cancer types by shrunken centroids of gene expression". Proc. Natl. Acad. Sci. USA., 99:6567-6572, 2002.
26. T. Lin, R. Liu and S. Chen, "Genetic Algorithms and Silhouette Measures Applied to Microarray Data Classification", Proceedings of 3rd Asia-Pacific Bioinforratics Conference, Singapore, 17-21 January 2005.
27. H. Pan, J. Zhu and D. Han, "Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data", Genomics Proteomics Bioinforratics. 1(4):279-87. November 2003.
28. C. H. Yang, L. Chuang and C. H. Yang. "IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data", Journal of Medical and Biological Engineering, 30(1):23-28. August 2009.
29. J. Cabrera, "Lung Cancer Classification System", Special Problem, unpublished.
30. University of Birmingham. "Supplementary Material for the paper: GALGO: An R Package For Multivariate Variable Selection Using Genetic Algorithms". http://www.bip,bham.ac.uk/vivo/galgo/AppNotesPaper. htm#Tocl27597486. Accessed: March 2014