We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 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

Review on Classification of Genes and Biomarker Identification

by Seema S, Hamida Honnalli
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 17
Year of Publication: 2013
Authors: Seema S, Hamida Honnalli
10.5120/11669-7266

Seema S, Hamida Honnalli . Review on Classification of Genes and Biomarker Identification. International Journal of Computer Applications. 68, 17 ( April 2013), 7-14. DOI=10.5120/11669-7266

@article{ 10.5120/11669-7266,
author = { Seema S, Hamida Honnalli },
title = { Review on Classification of Genes and Biomarker Identification },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 17 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number17/11669-7266/ },
doi = { 10.5120/11669-7266 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:28:05.936176+05:30
%A Seema S
%A Hamida Honnalli
%T Review on Classification of Genes and Biomarker Identification
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 17
%P 7-14
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recent advances in the DNA microarray technology have provided the ability to examine and measure the expression levels of thousands of genes simultaneously in an organism. In this technology each gene is recorded under different conditions or each gene is evaluated under a single environment but in different types of tissues. In the first case it is used in identification of functionally related genes where asthe second type of technology is helpful in classification of different types of tissues and identification of those genes whose expression levels are good diagnostic indicators. Different approaches have been applied to classify different datasets. However, the main challenges in this task is the availability of a smaller number of samples compared to huge number of genes and the noisy nature of biological data. This paper review on different techniques used to classify the genes and improved efficiency of biomarker identification due to these classifications.

References
  1. Guyon, I. , J. Weston, S. Barnhill and V. Vapnik, 2002,"Gene selection for cancer classification using support vector machines",Machine Learning, Volume 46, pp 389- 422.
  2. M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianiti, C. W. Sugnet, T. S. Furey, JrM. Ares, and Haussler, "Knowledge-based analysis of microarray gene expression data by using support vector machines", National Academic of Science. , USA, 97(1):262-267,2000.
  3. M. Stone, "Cross-valedictory choice and assessment of statistical predictions," Journal of the Royal Statistical Society:Series B, Vol. 36, no. 1, pp. 111–147, 1974.
  4. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of Machine Learning and Research, no. 3, pp. 1157–1182, Mar. 2003.
  5. Malik Yousef, Mohamed Ketany, Larry Manevitz, Louise C Showe and Michael K Showe"Classification and biomarker identification using gene networkmodules and support vector machines", BMC Bioinformatics 2009, Volume 10,pp:337
  6. R. Mallika, And V. Saravanan, "An SVM Based Classification Method For Cancer Data Using Minimum Microarray Gene Expressions", World Academy Of Science, Engineering And Technology 62, pp. 543-547,2010.
  7. A. Bharathi and A. M. Natarajan "Efficient Classification of Cancer using SupportVector Machines and Modified Extreme LearningMachine based on Analysis of Variance Features", American Journal of Applied Sciences Volume 8, No. 12, 1295-1301, 2011.
  8. J. Jacinth Salome and R. M. Suresh "An Effective Classification Technique for Microarray Gene Expression by Blending of LPP and SVM", European Journal of Scientific Research, Volume 64, No. 1 (2011), pp. 32-41
  9. Feng Chu and Lipo Wang, "Applying Rbf Neural Networks To Cancer Classification Based On Gene Expressions," International Joint Conference on Neural Networks, July 16-21,2006.
  10. Zainuddin. Z and Pauline. O, "Improved wavelet neural network for early diagnosis of cancer patients using microarray gene expression data", International Joint Conference on Neural Networks, 2009. IJCNN 2009.
  11. J. Khan, J. Wei, M. Ringner, L. Saal, and et. al. ," Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks", Nature Medicine,7(6):673-9, 2001.
  12. Jian J. Dai, Linh Lieu, David Rocke," Dimnesion reduction for classification with gene expression microarray data", Statistical applications in Genetics and molecular biology, Volume 5, Issue 1, 2006, pp 1-19
  13. Farid E Ahmed,"Artificial neural networks for diagnosis and survival prediction for colon cancer",Molecular cancer 2005, Volume 4, issue 29.
  14. RuiXu, Anagnostopoulos, G. C. And Wunsch, D. C. I. I. ,"Multiclass Cancer Classification Using Semi supervised 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.
  15. U. Scherf, D. Ross, M. Waltham, L. Smith, J. Lee, L. Tanabe, K. Kohn, W. Reinhold, T. Myers, D. Andrews, D. Scudiero, M. Eisen, E. Sausville, Y. Pommier, D. Botstein, P. Brown, and J. Weinstein, "A Gene Expression Database for the Molecular Pharmacology of Cancer," Nature Genetics, vol. 24, pp. 236-44, 2000.
  16. Jinn-Yi Yeh, Tai-Shi Wu, Min-Che Wu and Der-Ming Chang, "Applying Data Mining Techniques for Cancer Classification from Gene Expression Data", International Conference on Convergence Information Technology, 2007.
  17. M. MarufHossain ,Md. Rafiul Hassan andJames Bailey," A Novel Decision Tree Induction Algorithm Based on Receiver Operating Characteristics to Classify Gene Expression Data," The University of Melbourne, Australia.
  18. D. M. Green and J. M. Swets' "Signal detection theory and psychophysics" JohnWiley& Sons Inc. , New York,USA, 1966, ISBN:0-471-32420-5.
  19. Golub, T. R. , Slonim, D. K. , Tamayo, P. , Huard, C. , Gaasenbeek, M. ,Mesirov, J. P. , Coller, H. , Loh, M. , Downing, J. R. , Caligiuri, M. A. , Bloomfield,C. D. , and Lander, E. S. (1999). "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. " Science, Volume 286, pp 531–537
  20. J. Li and H. Liu. (2002) Kent Ridge Bio-Medical Data Set Repository: http://sdmc. lit. org. sg/GEDatasets/Datasets. html
  21. Wang, X. ,Gotoh O, "Microarray-Based Cancer Prediction Using Soft Computing Approach", Cancer Informatics, Volume 7, pp123–139, 2009.
  22. Kai-Bo Duan, Jagath C. Rajapakse, HaiyingWang,and Francisco Azuaje, "Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data", IEEE transactionsonnanobiosciences, Volume 4, No. 3, pp. 228-234,September 2005.
  23. Edmundo Bonilla Huerta, B´eatrice Duval, and Jin-Kao Hao "A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data", Evolutionary Workshops2006, Lecture Notes in Computer Science, pp. 34–44, 2006
  24. Oscar PicchiNetto, S´ergio Ricardo Nozawa Rafael Andr´es Rosales Mitrowsky, Alessandra Alaniz Macedo1, Jos´e Augusto Baranauskas, "Applying Decision Trees to Gene Expression Data from DNA Microarrays: A Leukemia Case Study", 20-23 July 2010, ISSN 2175-2761.
  25. Junying Zhang, Richard lee, Yue Joseph Wang, "Support Vector Machine Classifications for Microarray Expression Data Set," IEEE, 2003. Page 67 ISBN:0-7695-1957-1
  26. Wang X,Gotoh O. " Accurate molecular classification of cancer using simple rule", 2009 Oct 30; Volume 2, No. 64,2009.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining DNA microarray Support vector machine (SVM) Decision tree Neural network Biomarkers

Powered by PhDFocusTM