The week's pick
Random Articles
Reseach Article
Classification of Micro Array Gene Expression Data using Statistical Analysis Approach with Personalized Fuzzy Inference System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 31 - Number 1 |
| Year of Publication: 2011 |
| Authors: Tamilselvi Madeswaran, G.M.Kadhar Nawaz |
10.5120/3787-5215
|
Tamilselvi Madeswaran, G.M.Kadhar Nawaz . Classification of Micro Array Gene Expression Data using Statistical Analysis Approach with Personalized Fuzzy Inference System. International Journal of Computer Applications. 31, 1 ( October 2011), 5-12. DOI=10.5120/3787-5215
Abstract
In this paper we describe a method to classify the micro array gene expression data based on their tissue sample types. Normally dimensionality reduction is performed over the micro array gene expression data set. Here, we propose a statistical approach for extracting significant genes from the gene expression data set. But, the statistical approach does not correctly identify the important genes. Hence, the ultimate objective is to solve the drawbacks in dimensionality reduction as they have a direct impact on the robustness of the generated fuzzy rules. Consequently, the goal is to generate fuzzy rules based on dimensionality reduced data. Hence, fuzzy inference is selected in our approach for classification and the fuzzy rules are utilized to train the fuzzy inference system (FIS). The classification performance of the fuzzy inference system (FIS) is similar to that of other classifiers, but simpler and easier to interpret. The classification performance of the FIS classifier is compared over the existing Fuzzy Genetic, Fuzzy Neural Network ProbPCA and PCA classifiers. The classification performance of the proposed technique is evaluated over the cancer datasets of Acute myeloid leukemia (AML) and Acute Lymphoblastic Leukemia (ALL).
References
- Osmar, “Introduction to Data Mining”, In: Principles of Knowledge Discovery in Databases, CMPUT690, University of Alberta, Canada, 1999
- Kantardzic and Mehmed, “Data Mining: Concepts, Models, Methods, and Algorithms”, John Wiley & Sons, 2003
- Umarani and Punithavalli, "A Study on Effective Mining of Association Rules from Huge Databases", International Journal of Computer Science and Research, Vol. 1, No. 1, pp. 30-34, 2010
- Chieh-Yuan Tsai and Min-Hong Tsai, " A dynamic Web service based data mining process system", In Proceedings of the 5th IEEE International Conference on Computer and Information Technology, pp. 1033-1039, 21- 23 September, 2005
- Lamine M. Aouad, Nhien-An Le-Khac and Tahar M. Kechadi, "Distributed Frequent Itemsets Mining in Heterogeneous Platforms", Journal of Engineering, Computing and Architecture, Vol. 1, No. 2, 2007
- J. Han and M. Kamber, “Data Mining: Concepts and Techniques. Morgan Kaufman, San Francisco, 2000
- Bigus, “Data Mining with Neural Networks”, McGraw-Hill, 1996
- Klaus Julisch,” Data Mining for Intrusion Detection -A Critical Review”, In Proceedings of the IBM Research on application of Data Mining in Computer security, Chapter 1 , 2002
- Hewen Tang, Wei Fang and Yongsheng Cao, “A simple method of classification with VCL components”, In Proceedings of the 21st international CODATA Conference, 2008
- Umarani and Punithavalli, "A Study on Effective Mining of Association Rules From Huge Databases", International Journal of Computer Science and Research, Vol. 1, No. 1, pp. 30-34, 2010
- Yendrapalli, Basnet, Mukkamala and Sung, “Gene Selection for Tumor Classification Using Microarray Gene Expression Data", In Proceedings of the World Congress on Engineering, London, U.K., Vol. 1, 2007
- Sandrine Dudoit, Jane Fridlyand and Terence P. Speed, “Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data”, Journal of the American Statistical Association, Vol. 97, pp. 77-87, 2002
- Peterson and Ringner, “Analyzing Tumor Gene Expression Profiles”, Artificial Intelligence in Medicine, Vol. 28, No. 1, pp. 59-74, 2003
- Anandhavalli Gauthaman, "Analysis of DNA Microarray Data using Association Rules: A Selective Study", World Academy of Science, Engineering and Technology, Vol.42, pp.12-16, 2008
- Chintanu K. Sarmah, Sandhya Samarasinghe, Don Kulasiri and Daniel Catchpoole, "A Simple Affymetrix Ratio-transformation Method Yields Comparable Expression Level Quantifications with cDNA Data", World Academy of Science, Engineering and Technology, Vol. 61, pp.78-83, 2010
- Khlopova, Glazko and Glazko, “Differentiation of Gene Expression Profiles Data for Liver and Kidney of Pigs”, World Academy of Science, Engineering and Technology, Vol. 55, pp. 267-270, 2009
- Ahmad m. Sarhan, "Cancer classification based on microarray gene expression data using DCT and ANN", Journal of Theoretical and Applied Information Technology, Vol. 6, No. 2, pp. 207-216, 2009
- Ying Xu, Victor Olman and Dong Xu, "Minimum Spanning Trees for Gene Expression Data Clustering", Genome Informatics, Vol. 12, pp. 24–33, 2001
- Lucila Ohno-Machado, Staal Vinterbo and Griffin Weber, "Classification of Gene Expression Data Using Fuzzy Logic", Journal of Intelligent & Fuzzy Systems, Vol. 12, No. 1, pp. 19-24, January 2002
- Li-Yeh Chuang, Cheng-Hong Yang and Li-Cheng Jin, "Classification Of Multiple Cancer Types Using Fuzzy Support Vector Machines And Outlier Detection Methods", Biomedical Engineering applications, Basis and Communications, Vol. 17, No. 6, pp. 300-308, December 2005
- Edmundo Bonilla Huerta, Beatrice Duval and Jin-Kao Hao, "A hybrid GA/SVM approach for gene selection and classification of micro array data", In Lecture Notes in Computer Science, pp. 34-44, Springer, 2006
- Hieu Trung Huynh, Jung-Ja Kimand Yonggwan Won, "Classification Study on DNA Micro array with Feed forward Neural Network Trained by Singular Value Decomposition", International Journal of Bio- Science and Bio- Technology Vol. 1, No. 1, pp. 17-24, December, 2009
- Pradipta Maji and Sankar K. Pal, "Fuzzy–Rough Sets for Information Measures and Selection of Relevant Genes from Micro array Data", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 40, No. 3, pp. 741-752, June 2010
- Venkatesh and Thangaraj, "Investigation of Micro Array Gene Expression Using Linear Vector Quantization for Cancer", International Journal on Computer Science and Engineering, Vol. 02, No. 06, pp. 2114-2116, 2010.
Index Terms
Keywords