CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Human Liver Cancer Classification using Microarray Gene Expression Data

by P. Rajeswari, G. Sophia Reena
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 34 - Number 6
Year of Publication: 2011
Authors: P. Rajeswari, G. Sophia Reena
10.5120/4104-5848

P. Rajeswari, G. Sophia Reena . Human Liver Cancer Classification using Microarray Gene Expression Data. International Journal of Computer Applications. 34, 6 ( November 2011), 25-37. DOI=10.5120/4104-5848

@article{ 10.5120/4104-5848,
author = { P. Rajeswari, G. Sophia Reena },
title = { Human Liver Cancer Classification using Microarray Gene Expression Data },
journal = { International Journal of Computer Applications },
issue_date = { November 2011 },
volume = { 34 },
number = { 6 },
month = { November },
year = { 2011 },
issn = { 0975-8887 },
pages = { 25-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume34/number6/4104-5848/ },
doi = { 10.5120/4104-5848 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:20:23.972964+05:30
%A P. Rajeswari
%A G. Sophia Reena
%T Human Liver Cancer Classification using Microarray Gene Expression Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 34
%N 6
%P 25-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cancer is one of the dreadful diseases, which causes considerable death rate in humans. Cancer is featured by an irregular, unmanageable growth that may demolish and attack neighboring healthy body tissues or somewhere else in the body. There are dissimilar techniques lives for the naming of cancer but none of those techniques afford considerable accuracy of detection. Therefore a new method is highly essential for the cancer classification with improved accuracy. Gene expression profiling by microarray method has been emerged as an efficient technique for classification and diagnostic prediction of cancer nodules. In recent times, DNA microarray technique has gained more attraction in both scientific and in industrial fields. The DNA microarrays are utilized in this paper for the purpose of identifying the presence of cancer. Statistical ranking has also been used for effective cancer classification. The most widely used ranking schemes are ANOVA, T-score and Enrichment Score. But, these existing techniques suffer from the drawbacks of less accuracy, complexity etc. This paper uses liver cancer data set for experimentation of the proposed technique. The classifier used here is SVM and FNN. The experimental results shows that the proposed technique has the ability to classify the cancer cells significantly when compared to the conventional methods of cancer classification.

References
  1. Hong-Hee Won; Sung-Bae Cho; “Paired neural network with negatively correlated features for cancer classification in DNA gene expression profiles”, Proceedings of the International Joint Conference on Neural Networks, Vol. 3, Pp. 1708 – 1713, 2003.
  2. Hu, Y.; Ashenayi, K.; Veltri, R.; O'Dowd, G.; Miller, G.; Hurst, R.; Bonner, R.; “A comparison of neural network and fuzzy c-means methods in bladder cancer cell classification”, IEEE International Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vol. 6, Pp. 3461 – 3466, 1994.
  3. Bevilacqua, V.; Mastronardi, G.; Menolascina, F.; Pannarale, P.; Pedone, A.; “A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast Cancer Classification Problem”, International Joint Conference on Neural Networks (IJCNN '06), Pp. 1958 – 1965, 2006.
  4. Joshi, D.M.; Rana, N.K.; Misra, V.M.; “Classification of Brain Cancer using Artificial Neural Network”, International Conference on Electronic Computer Technology (ICECT), Pp. 112 – 116, 2010.
  5. Land, W.H., Jr.; Masters, T.; Lo, J.Y.; McKee, D.W.; “Application of evolutionary computation and neural network hybrids for breast cancer classification using mammogram and history data”, Proceedings of the 2001 Congress on Evolutionary Computation, Vol. 2, Pp. 1147 – 1154, 2001.
  6. Kermani, B.G.; White, M.W.; Nagle, H.T.; “Feature extraction by genetic algorithms for neural networks in breast cancer classification”, IEEE 17th Annual Conference Engineering in Medicine and Biology Society, Vol. 1, Pp. 831 – 832, 1995.
  7. Feng Chu; Lipo Wang; “Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions”, International Joint Conference on Neural Networks (IJCNN '06), Pp. 1930 – 1934, 2006.
  8. Feng Chu; Wei Xie; Lipo Wang; “Gene selection and cancer classification using a fuzzy neural network”, IEEE Annual Meeting of the Fuzzy Information Processing (NAFIPS '04), Vol. 2, Pp. 555 – 559, 2004.
  9. Rui Xu; Anagnostopoulos, G.C.; Wunsch, D.C.I.I.; “Multiclass Cancer Classification Using Semisupervised Ellipsoid ARTMAP and Particle Swarm Optimization with Gene Expression Data”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 4, No. 1, Pp. 65 – 77, 2007.
  10. Kocur, C.M.; Rogers, S.K.; Myers, L.R.; Burns, T.; Kabrisky, M.; Hoffmeister, J.W.; Bauer, K.W.; Steppe, J.M.; “Using neural networks to select wavelet features for breast cancer diagnosis”, IEEE Engineering in Medicine and Biology Magazine, Vol. 15, No. 3, Pp. 95 - 102, 1996.
  11. Schnorrenberg, F.; Tsapatsoulis, N.; Pattichis, C.S.; Schizus, C.N.; Kollias, S.; Vassiliou, M.; Adamou, A.; Kyriacou, K.; “Improved detection of breast cancer nuclei using modular neural networks”, IEEE Engineering in Medicine and Biology Magazine, Vol. 19, No. 1, Pp. 48 – 63, 2000.
  12. Azuaje, F.; “A computational neural approach to support the discovery of gene function and classes of cancer”, IEEE Transactions on Biomedical Engineering, Vol. 48, No. 3, Pp. 332 – 339, 2001.
  13. Moallemi, C.; “Classifying cells for cancer diagnosis using neural networks”, IEEE Expert, Vol. 6, No. 6, Pp. 8, 10 – 12, 1991.
  14. Yuchun Tang; Yan-Qing Zhang; Zhen Huang; Xiaohua Hu; Yichuan Zhao; “Recursive Fuzzy Granulation for Gene Subsets Extraction and Cancer Classification”, IEEE Transactions on Information Technology in Biomedicine, Vol. 2, No. 6, Pp. 723 – 730, 2008.
  15. Kai-Bo Duan; Rajapakse, J.C.; Haiying Wang; Azuaje, F.; “Multiple SVM-RFE for gene selection in cancer classification with expression data”, IEEE Transactions on NanoBioscience, Vol. 4, No. 3, Pp. 228 – 234, 2005.
  16. Peng Qiu; Wang, Z.J.; Liu, K.J.R.; “Genomic processing for cancer classification and prediction - Abroad review of the recent advances in model-based genomoric and proteomic signal processing for cancer detection”, IEEE Signal Processing Magazine, Vol. 24, Vo. 1, Pp. 100 – 110, 2007.
  17. Ghorai, S.; Mukherjee, A.; Sengupta, S.; Dutta, P.K.; “Cancer Classification from Gene Expression Data by NPPC Ensemble”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 8, No. 3, Pp. 659 – 671, 2011.
  18. Jiexun Li; Hua Su; Hsinchun Chen; Futscher, B.W.; “Optimal Search-Based Gene Subset Selection for Gene Array Cancer Classification”, IEEE Transactions on Information Technology in Biomedicine, Vol. 11, No. 4, Pp. 398 – 405, 2007.
  19. Toure, A.; Basu, M.; “Application of neural network to gene expression data for cancer classification”, Proceedings International Joint Conference on Neural Networks (IJCNN '01), Vol. 1, Pp. 583 – 587, 2001.
  20. Sehgal, M.S.B.; Gondal, I.; Dooley, L.; “Support vector machine and generalized regression neural network based classification fusion models for cancer diagnosis”, Fourth International Conference on Hybrid Intelligent Systems (HIS '04), Pp. 49 – 54, 2004.
  21. A.O. Hero, “Gene selection and ranking with microarray data,” Seventh International Symposium on Signal Processing and its Applications, Vol. 1, pp. 457 – 464, 2003.
  22. Chen Liao, Shutao Li, Zhiyuan Luo, “Gene Selection for Cancer Classification using Wilcoxon Rank Sum Test and Support Vector Machine,” International Conference on Computational Intelligence and Security, Vol. 1, pp. 368 – 373, 2006.
  23. P. Broberg, “Statistical methods for ranking differentially expressed genes,” Genome Biology, Vol.4, No. 6, 2003.
  24. Han-Yu Chuang, Hong fang Liu Brown, S. McMunn-Coffran, C. Cheng-Yan Kao, D. F. Hsu, “Identifying significant genes from microarray data,” Fourth IEEE Symposium on Bioinformatics and Bioengineering, pp. 358 – 365, 2004.
  25. Jin-Hyuk Hong, Sung-Bae Cho, “Cancer classification with incremental gene selection based on DNA microarray data,” CIBCB '08, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 70 – 74, 2008.
  26. Hengpraprohm, S.; Chongstitvatana, P.; “Selecting Informative Genes from Microarray Data for Cancer Classification with Genetic Programming Classifier Using K-Means Clustering and SNR Ranking”, Frontiers in the Convergence of Bioscience and Information Technologies (FBIT), Pp. 211 – 218, 2007.
  27. Chen Liao; Shutao Li; Zhiyuan Luo; “Gene Selection for Cancer Classification using Wilcoxon Rank Sum Test and Support Vector Machine”, International Conference on Computational Intelligence and Security, Vol. 1, Pp. 368 – 373, 2006.
  28. Yu, Hualong; Sen Xu; “Simple rule-based ensemble classifiers for cancer DNA microarray data classification”, International Conference on Computer Science and Service System (CSSS), Pp. 2555 – 2558, 2011.
  29. Lipo Wang; Feng Chu; Wei Xie; “Accurate Cancer Classification Using Expressions of Very Few Genes”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 4, No. 1, Pp. 40 – 53, 2007.
  30. Saravanan, V.; Mallika, R.; “An Effective Classification Model for Cancer Diagnosis Using Micro Array Gene Expression Data”, International Conference on Computer Engineering and Technology (ICCET '09), Vol. 1, Pp. 137 – 141, 2009.
  31. Hand, David, Heikki Mannila, and Padhraic Smyth, Principles of Data Mining, MIT Press 2001.
  32. Meyer T and Hart IR (1998) “Mechanisms of Tumour Metastasis”, European Journal of Cancer 34: 214–221.
  33. http://genome-www.stanford.edu/hcc/
Index Terms

Computer Science
Information Sciences

Keywords

Microarray Dataset Enrichment Score Correlation Based Ranking MAPSTD SVM FNN

Powered by PhDFocusTM