title: Spectral analysis of microarray gene expression time series data of Plasmodium falciparum.
abstract: We propose a new strategy to analyse the periodicity of gene expression profiles using Singular Spectrum Analysis (SSA) and Autoregressive (AR) model based spectral estimation. By combining the advantages of SSA and AR modelling, more periodic genes are extracted in the Plasmodium falciparum data set, compared with the classical Fourier analysis technique. We are able to identify more gene targets for new drug discovery, and by checking against the seven well-known malaria vaccine candidates, we have found five additional genes that warrant further biological verification.
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. dlp2001@ies.ustb.edu.cn
authors.forename: Liping
authors.initials: L
authors.lastname: Du
authors.forename: Shuanhu
authors.initials: S
authors.lastname: Wu
authors.forename: Alan Wee-Chung
authors.initials: AW
authors.lastname: Liew
authors.forename: David K
authors.initials: DK
authors.lastname: Smith
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010963:Plasmodium falciparum / Q000378:metabolism*; D015800:Protozoan Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D015800:Protozoan Proteins