title: PCA-HPR: a principle component analysis model for human promoter recognition.
abstract: We describe a promoter recognition method named PCA-HPR to locate eukaryotic promoter regions and predict transcription start sites (TSSs). We computed codon (3-mer) and pentamer (5-mer) frequencies and created codon and pentamer frequency feature matrices to extract informative and discriminative features for effective classification. Principal component analysis (PCA) is applied to the feature matrices and a subset of principal components (PCs) are selected for classification. Our system uses three neural network classifiers to distinguish promoters versus exons, promoters versus introns, and promoters versus 3' un-translated region (3'UTR). We compared PCA-HPR with three well-known existing promoter prediction systems such as DragonGSF, Eponine and FirstEF. Validation shows that PCA-HPR achieves the best performance with three test sets for all the four predictive systems.
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. sgusico@hotmail.com
authors.forename: Xiaomeng
authors.initials: X
authors.lastname: Li
authors.forename: Jia
authors.initials: J
authors.lastname: Zeng
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
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