Principal component analysis based unsupervised feature extraction applied to budding yeast temporally periodic gene expression.

ABSTRACT:

Background

The recently proposed principal component analysis (PCA) based unsupervised feature extraction (FE) has successfully been applied to various bioinformatics problems ranging from biomarker identification to the screening of disease causing genes using gene expression/epigenetic profiles. However, the conditions required for its successful use and the mechanisms involved in how it outperforms other supervised methods is unknown, because PCA based unsupervised FE has only been applied to challenging (i.e. not well known) problems.

Results

In this study, PCA based unsupervised FE was applied to an extensively studied organism, i.e., budding yeast. When applied to two gene expression profiles expected to be temporally periodic, yeast metabolic cycle (YMC) and yeast cel

SUBMITTER: Taguchi YH 

PROVIDER: S-EPMC4928327 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature