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Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

Y-H. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Yoshiki Murakami, Akira Okamoto

Source Title: Big Data Analytics in Bioinformatics and Healthcare

ISBN13: 9781466666115|ISBN10: 1466666110|EISBN13: 9781466666122

DOI: 10.4018/978-1-4666-6611-5.ch007

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MLA

Taguchi, Y-H., et al. "Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics." Big Data Analytics in Bioinformatics and Healthcare, edited by Baoying Wang, et al., IGI Global, 2015, pp. 138-162. https://doi.org/10.4018/978-1-4666-6611-5.ch007

APA

Taguchi, Y., Iwadate, M., Umeyama, H., Murakami, Y., & Okamoto, A. (2015). Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics. In B. Wang, R. Li, & W. Perrizo (Eds.), Big Data Analytics in Bioinformatics and Healthcare (pp. 138-162). IGI Global. https://doi.org/10.4018/978-1-4666-6611-5.ch007

Chicago

Taguchi, Y-H., et al. "Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics." In Big Data Analytics in Bioinformatics and Healthcare, edited by Baoying Wang, Ruowang Li, and William Perrizo, 138-162. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6611-5.ch007

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Abstract

Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE—detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes—are discussed.

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Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

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