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

摘要

BackgroundThe 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.