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 (Tokyo University of Science, Chiba, Japan), Katsutoshi Kanamori (Tokyo University of Science, Chiba, Japan) and Hayato Ohwada (Tokyo University of Science, Chiba, Japan)
DOI: 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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Dataset

We obtained datasets of DNA microarray from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) (Edgar et al., 2002). The Gene Expression Omnibus contains numerous datasets of DNA microarray obtained from different species and environments of experiments. Since we had to select datasets obtained from homo sapiens, we conducted a search for such datasets using the following steps:

  • 1.

    We searched the phrase “Alzheimer’s disease” and checked the box of “DataSets” to set Entry type, and limited the sample organism as “homo sapiens” on the Gene Expression Omnibus;

  • 2.

    As a result, we obtained 13 papers discussing Alzheimer’s disease, and we downloaded datasets of DNA microarray referenced in these papers;

  • 3.

    We selected datasets including samples from human brain cells.

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