title: Predicting housekeeping genes based on Fourier analysis.
abstract: Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs.
authors.affiliation: Bioinformatics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Bo
authors.initials: B
authors.lastname: Dong
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Chen
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.forename: Yunfei
authors.initials: Y
authors.lastname: Wang
authors.forename: Shunmin
authors.initials: S
authors.lastname: He
authors.forename: Runsheng
authors.initials: R
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D005583:Fourier Analysis; D020869:Gene Expression Profiling; D006367:HeLa Cells; D006801:Humans; D015203:Reproducibility of Results; D013997:Time Factors