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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...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics Publishing Group
2006
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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. |
format | Text |
id | pubmed-1891703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Biomedical Informatics Publishing Group |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT taylorpauld multiclasssubcellularlocationpredictionforbacterialproteins AT attwoodteresak multiclasssubcellularlocationpredictionforbacterialproteins AT flowerdarrenr multiclasssubcellularlocationpredictionforbacterialproteins |