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Gene Selection from Microarray Data for Alzheimer's Disease Using Random Forest
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Author(s): Kazutaka Nishiwaki (Tokyo University of Science, Chiba, Japan), Katsutoshi Kanamori (Tokyo University of Science, Chiba, Japan)and Hayato Ohwada (Tokyo University of Science, Chiba, Japan)
Copyright: 2020
Pages: 14
Source title:
Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch070
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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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