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PCA-HPR: A principle component analysis model for human promoter recognition

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 f...

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Detalles Bibliográficos
Autores principales: Li, Xiaomeng, Zeng, Jia, Yan, Hong
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics Publishing Group 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2533055/
https://www.ncbi.nlm.nih.gov/pubmed/18795109
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author Li, Xiaomeng
Zeng, Jia
Yan, Hong
author_facet Li, Xiaomeng
Zeng, Jia
Yan, Hong
author_sort Li, Xiaomeng
collection PubMed
description 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.
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spelling pubmed-25330552008-09-15 PCA-HPR: A principle component analysis model for human promoter recognition Li, Xiaomeng Zeng, Jia Yan, Hong Bioinformation Prediction Model 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. Biomedical Informatics Publishing Group 2008-06-19 /pmc/articles/PMC2533055/ /pubmed/18795109 Text en © 2008 Biomedical Informatics Publishing Group This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Prediction Model
Li, Xiaomeng
Zeng, Jia
Yan, Hong
PCA-HPR: A principle component analysis model for human promoter recognition
title PCA-HPR: A principle component analysis model for human promoter recognition
title_full PCA-HPR: A principle component analysis model for human promoter recognition
title_fullStr PCA-HPR: A principle component analysis model for human promoter recognition
title_full_unstemmed PCA-HPR: A principle component analysis model for human promoter recognition
title_short PCA-HPR: A principle component analysis model for human promoter recognition
title_sort pca-hpr: a principle component analysis model for human promoter recognition
topic Prediction Model
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2533055/
https://www.ncbi.nlm.nih.gov/pubmed/18795109
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