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MapReduce based parallel gene selection method

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Abstract

Microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although many genes are expressed in a sample tissue, most of these are irrelevant or insignificant for clinical diagnosis or disease classification because of missing values and noises. Thus, finding a small, closely related gene set to accurately classify disease cells is an important research problem. At the same time, scalable gene selection methods are required for microarray data analysis due to rapidly increasing volume of microarray data. In this paper, we propose a scalable parallel gene selection method using the M a p R e u d c e programming model. The proposed method utilizes the kNN classifier algorithm for evaluating classification accuracy and uses four real and three synthetic datasets for experiments. Experimental results show that the proposed method can offer good scalability on large data with increasing number of nodes and it can also provide higher classification accuracy rather than using whole gene set for classification.

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Acknowledgments

This work was supported by a grant from the Kyung Hee University in 2013 (KHU-20130441)

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Authors and Affiliations

  1. Computer Engineering Department, Kyung Hee University, Seoul, South Korea

    A. K. M. Tauhidul Islam, Byeong-Soo Jeong, A. T. M. Golam Bari, Chae-Gyun Lim & Seok-Hee Jeon

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  1. A. K. M. Tauhidul Islam
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  2. Byeong-Soo Jeong
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  3. A. T. M. Golam Bari
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  4. Chae-Gyun Lim
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  5. Seok-Hee Jeon
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Correspondence to Byeong-Soo Jeong.

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Islam, A.K.M.T., Jeong, BS., Bari, A.T.M.G. et al. MapReduce based parallel gene selection method. Appl Intell 42, 147–156 (2015). https://doi.org/10.1007/s10489-014-0561-x

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  • DOI: https://doi.org/10.1007/s10489-014-0561-x

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