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Efficient Retrieval Technique for Microarray Gene Expression

Efficient Retrieval Technique for Microarray Gene Expression

J. Jacinth Salome
Copyright: © 2012 |Volume: 2 |Issue: 2 |Pages: 9
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781466612624|DOI: 10.4018/ijirr.2012040104
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MLA

Salome, J. Jacinth. "Efficient Retrieval Technique for Microarray Gene Expression." IJIRR vol.2, no.2 2012: pp.43-51. http://doi.org/10.4018/ijirr.2012040104

APA

Salome, J. J. (2012). Efficient Retrieval Technique for Microarray Gene Expression. International Journal of Information Retrieval Research (IJIRR), 2(2), 43-51. http://doi.org/10.4018/ijirr.2012040104

Chicago

Salome, J. Jacinth. "Efficient Retrieval Technique for Microarray Gene Expression," International Journal of Information Retrieval Research (IJIRR) 2, no.2: 43-51. http://doi.org/10.4018/ijirr.2012040104

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Abstract

The DNA mciroarray gene data is in the expression levels of thousands of genes for a small amount of samples. From the microarray gene data, the process of extracting the required knowledge remains an open challenge. Acquiring knowledge is the intricacy in such types of gene data, though number of researches is arising in order to acquire information from these gene data. In order to retrieve the required information, gene classification is vital; however, the task is complex because of the data characteristics, high dimensionality and smaller sample size. Initially, the dimensionality diminution process is carried out in order to shrink the microarray data without losing information with the aid of LPP and PCA techniques and utilized for information retrieval. In this paper, we propose an effective gene retrieval technique based on LPP and PCA called LPCA. The technique like LPP and PCA is chosen for the dimensionality reduction for efficient retrieval of microarray gene data. An application of microarray gene data is included with classification by SVM. SVM is trained by the dimensionality reduced gene data for effective classification. A comparative study is made with these dimensionality reduction techniques.

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