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

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Author(s): Y-H. Taguchi (Chuo University, Japan), Mitsuo Iwadate (Chuo University, Japan), Hideaki Umeyama (Chuo University, Japan), Yoshiki Murakami (Osaka City University, Japan)and Akira Okamoto (Aichi University of Education, Japan)
Copyright: 2015
Pages: 25
Source title: Big Data Analytics in Bioinformatics and Healthcare
Source Author(s)/Editor(s): Baoying Wang (Waynesburg University, USA), Ruowang Li (Pennsylvania State University, USA)and William Perrizo (North Dakota State University, USA)
DOI: 10.4018/978-1-4666-6611-5.ch007

Keywords: Bioinformatics / Medical Information Science Reference / Medicine & Healthcare

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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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