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Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors
To identify divergent seven-transmembrane receptor (7TMR) candidates from the Arabidopsis thaliana genome, multiple protein classification methods were combined, including both alignment-based and alignment-free classifiers. This resolved problems in optimally training individual classifiers using l...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1794564/ https://www.ncbi.nlm.nih.gov/pubmed/17064408 http://dx.doi.org/10.1186/gb-2006-7-10-r96 |
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author | Moriyama, Etsuko N Strope, Pooja K Opiyo, Stephen O Chen, Zhongying Jones, Alan M |
author_facet | Moriyama, Etsuko N Strope, Pooja K Opiyo, Stephen O Chen, Zhongying Jones, Alan M |
author_sort | Moriyama, Etsuko N |
collection | PubMed |
description | To identify divergent seven-transmembrane receptor (7TMR) candidates from the Arabidopsis thaliana genome, multiple protein classification methods were combined, including both alignment-based and alignment-free classifiers. This resolved problems in optimally training individual classifiers using limited and divergent samples, and increased stringency for candidate proteins. We identified 394 proteins as 7TMR candidates and highlighted 54 with corresponding expression patterns for further investigation. |
format | Text |
id | pubmed-1794564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-17945642007-02-08 Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors Moriyama, Etsuko N Strope, Pooja K Opiyo, Stephen O Chen, Zhongying Jones, Alan M Genome Biol Method To identify divergent seven-transmembrane receptor (7TMR) candidates from the Arabidopsis thaliana genome, multiple protein classification methods were combined, including both alignment-based and alignment-free classifiers. This resolved problems in optimally training individual classifiers using limited and divergent samples, and increased stringency for candidate proteins. We identified 394 proteins as 7TMR candidates and highlighted 54 with corresponding expression patterns for further investigation. BioMed Central 2006 2006-10-25 /pmc/articles/PMC1794564/ /pubmed/17064408 http://dx.doi.org/10.1186/gb-2006-7-10-r96 Text en Copyright © 2006 Moriyama et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Moriyama, Etsuko N Strope, Pooja K Opiyo, Stephen O Chen, Zhongying Jones, Alan M Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
title | Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
title_full | Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
title_fullStr | Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
title_full_unstemmed | Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
title_short | Mining the Arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
title_sort | mining the arabidopsis thaliana genome for highly-divergent seven transmembrane receptors |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1794564/ https://www.ncbi.nlm.nih.gov/pubmed/17064408 http://dx.doi.org/10.1186/gb-2006-7-10-r96 |
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