Classification of Gene Expression Data by Gene Combination using Fuzzy Logic

Daxa Ghadiya; Prof. Ankit Kharwar; Prof. Monali Gandhi

Classification of Gene Expression Data by Gene Combination using Fuzzy Logic

October 2015

Vol-1, Issue-2

Paper ID: 1137

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Data mining

Keywords

Feature selection classification clustering T-test Cancer classification gene expression fuzzy neural networks supports vector machines

Abstract

The goal of microarray experiments is to identify genes that are differentially transcribed with respect to different biological conditions of cell cultures and samples. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is improved/ modified in this report to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes mare filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.

License

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Author Information

#	Name	Institute / Affiliation
1	Daxa Ghadiya	CGPIT,BARDOLI
2	Prof. Ankit Kharwar	CGPIT,BARDOLI
3	Prof. Monali Gandhi	CGPIT,BARDOLI

How to Cite

Use the following formats to cite this article in your research.

APA Style

Ghadiya, Daxa, Kharwar, Prof. Ankit, & Gandhi, Prof. Monali (2015). Classification of Gene Expression Data by Gene Combination using Fuzzy Logic. International Journal of Advance Research and Innovative Ideas In Education, 1(2), 43-48.

MLA Style

Ghadiya, Daxa, et al. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic." International Journal of Advance Research and Innovative Ideas In Education, vol. 1, no. 2, 2015, pp. 43-48.

IEEE Style

Daxa Ghadiya, Prof. Ankit Kharwar, and Prof. Monali Gandhi, "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic," International Journal of Advance Research and Innovative Ideas In Education, vol. 1, no. 2, pp. 43-48, 2015.

Vancouver Style

Ghadiya Daxa, Kharwar Prof. Ankit, Gandhi Prof. Monali. Classification of Gene Expression Data by Gene Combination using Fuzzy Logic. International Journal of Advance Research and Innovative Ideas In Education. 2015;1(2):43-48.

Harvard Style

Ghadiya, Daxa, Kharwar, Prof. Ankit, & Gandhi, Prof. Monali (2015) 'Classification of Gene Expression Data by Gene Combination using Fuzzy Logic', International Journal of Advance Research and Innovative Ideas In Education, 1(2), pp. 43-48.

Chicago Style

Ghadiya, Daxa, Prof. Ankit Kharwar, and Prof. Monali Gandhi. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic." International Journal of Advance Research and Innovative Ideas In Education 1, no. 2 (2015): 43-48.

Turabian Style

Ghadiya, Daxa, Prof. Ankit Kharwar, and Prof. Monali Gandhi. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic." International Journal of Advance Research and Innovative Ideas In Education 1, no. 2 (2015): 43-48.