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Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest

Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest

Kazutaka Nishiwaki, Katsutoshi Kanamori, Hayato Ohwada
Copyright: © 2017 |Volume: 9 |Issue: 2 |Pages: 17
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781522512721|DOI: 10.4018/IJSSCI.2017040102
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MLA

Nishiwaki, Kazutaka, et al. "Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest." IJSSCI vol.9, no.2 2017: pp.14-30. http://doi.org/10.4018/IJSSCI.2017040102

APA

Nishiwaki, K., Kanamori, K., & Ohwada, H. (2017). Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest. International Journal of Software Science and Computational Intelligence (IJSSCI), 9(2), 14-30. http://doi.org/10.4018/IJSSCI.2017040102

Chicago

Nishiwaki, Kazutaka, Katsutoshi Kanamori, and Hayato Ohwada. "Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest," International Journal of Software Science and Computational Intelligence (IJSSCI) 9, no.2: 14-30. http://doi.org/10.4018/IJSSCI.2017040102

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Abstract

A significant amount of microarray gene expression data is available on the Internet, and researchers are allowed to analyze such data freely. However, microarray data includes thousands of genes, and analysis using conventional techniques is too difficult. Therefore, selecting informative gene(s) from high-dimensional data is very important. In this study, the authors propose a gene selection method using random forest as a machine learning technique. They applied this method to microarray data on Alzheimer's disease and conducted an experiment to rank genes. The authors' results indicated some genes that have been investigated for their relevance to Alzheimer's disease, proving that their proposed cognitive method was successful in finding disease-related genes using microarray data.

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