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Multi-class subcellular location prediction for bacterial proteins

Two algorithms, based on Bayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 1...

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Detalles Bibliográficos
Autores principales: Taylor, Paul D, Attwood, Teresa K, Flower, Darren R
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics Publishing Group 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1891703/
https://www.ncbi.nlm.nih.gov/pubmed/17597904
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author Taylor, Paul D
Attwood, Teresa K
Flower, Darren R
author_facet Taylor, Paul D
Attwood, Teresa K
Flower, Darren R
author_sort Taylor, Paul D
collection PubMed
description Two algorithms, based on Bayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection.
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spelling pubmed-18917032007-06-27 Multi-class subcellular location prediction for bacterial proteins Taylor, Paul D Attwood, Teresa K Flower, Darren R Bioinformation Prediction Model Two algorithms, based on Bayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection. Biomedical Informatics Publishing Group 2006-11-24 /pmc/articles/PMC1891703/ /pubmed/17597904 Text en © 2005 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
Taylor, Paul D
Attwood, Teresa K
Flower, Darren R
Multi-class subcellular location prediction for bacterial proteins
title Multi-class subcellular location prediction for bacterial proteins
title_full Multi-class subcellular location prediction for bacterial proteins
title_fullStr Multi-class subcellular location prediction for bacterial proteins
title_full_unstemmed Multi-class subcellular location prediction for bacterial proteins
title_short Multi-class subcellular location prediction for bacterial proteins
title_sort multi-class subcellular location prediction for bacterial proteins
topic Prediction Model
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1891703/
https://www.ncbi.nlm.nih.gov/pubmed/17597904
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