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Análisis de Pseudomonas fitopatógenas usando métodos inteligentes de aprendizaje un enfoque general sobre taxonomía y análisis de ácidos grasos dentro del género Pseudomonas

Bram Slabbinck UGent, Bernard De Baets UGent, Peter Dawyndt UGent and Paul De Vos UGent (2010) REVISTA MEXICANA DE FITOPATOLOGIA. 28(1).
abstract
The identification of plant-pathogenic bacteria is often of high importance. In this paper, we evaluate the identification of plant-pathogenic species within the genus Pseudomonas by fatty acid methyl ester (FAME) analysis. Starting from a FAME database, high quality data sets were generated. Two research questions were investigated: can plant-pathogenic Pseudomonas species be discriminated from each other and can the group of plant-pathogenic Pseudomonas species be distinguished from the group of non-plant-pathogenic Pseudomonas species. In a first stage, a principal component analysis was performed to evaluate the variability within the data. Secondly, the machine learning method Random Forests was evaluated for identification purposes. This intelligent method allows to learn from the variability and patterns in the data and to improve the species identification. The principal component analysis of plant-pathogenic species clearly showed overlapping data clouds. A Random Forests model was developed that achieved a species identification performance of 71.1%. Discriminating the group of plant-pathogenic species from the group of non-plant- pathogenic species was more straightforward, given by the Random Forests identification performance of 85.9%. Moreover, it was shown that a statistical relation exists between the fatty acid profiles and plant pathogenesis.
Please use this url to cite or link to this publication:
author
organization
alternative title
Analysis of plant-pathogenic Pseudomonas species using intelligent learning methods : a general focus on taxonomy and fatty acid analysis within the genus Pseudomonas
year
type
journalArticle (original)
publication status
published
subject
keyword
non-pathogenic bacteria, Diagnosis
journal title
REVISTA MEXICANA DE FITOPATOLOGIA
Rev. Mex. Fitopatol.
volume
28
issue
1
pages
16 pages
ISSN
0185-3309
language
Spanish
UGent publication?
yes
classification
A2
additional info
bilingual Spanish-English article
copyright statement
I have transferred the copyright for this publication to the publisher
id
1008724
handle
http://hdl.handle.net/1854/LU-1008724
alternative location
http://sociedadmexicanadefitopatologia.org/archives/rmf_28_1_articulo_1.pdf
date created
2010-07-12 08:57:34
date last changed
2010-08-17 15:48:37
@article{1008724,
  abstract     = {The identification of plant-pathogenic bacteria is often of high importance. In this paper, we evaluate the identification of plant-pathogenic species within the genus Pseudomonas by fatty acid methyl ester (FAME) analysis. Starting from a FAME database, high quality data sets were generated. Two research questions were investigated: can plant-pathogenic Pseudomonas species be discriminated from each other and can the group of plant-pathogenic Pseudomonas species be distinguished from the group of non-plant-pathogenic Pseudomonas species. In a first stage, a principal component analysis was performed to evaluate the variability within the data. Secondly, the machine learning method Random Forests was evaluated for identification purposes. This intelligent method allows to learn from the variability and patterns in the data and to improve the species identification. The principal component analysis of plant-pathogenic species clearly showed overlapping data clouds. A Random Forests model was developed that achieved a species identification performance of 71.1\%. Discriminating the group of plant-pathogenic species from the group of non-plant- pathogenic species was more straightforward, given by the Random Forests identification performance of 85.9\%. Moreover, it was shown that a statistical relation exists between the fatty acid profiles and plant pathogenesis.},
  author       = {Slabbinck, Bram and De Baets, Bernard and Dawyndt, Peter and De Vos, Paul},
  issn         = {0185-3309},
  journal      = {REVISTA MEXICANA DE FITOPATOLOGIA},
  keyword      = {non-pathogenic bacteria,Diagnosis},
  language     = {spa},
  number       = {1},
  pages        = {16},
  title        = {An{\'a}lisis de Pseudomonas fitopat{\'o}genas usando m{\'e}todos inteligentes de aprendizaje un enfoque general sobre taxonom{\'i}a y an{\'a}lisis de {\'a}cidos grasos dentro del g{\'e}nero Pseudomonas},
  url          = {http://sociedadmexicanadefitopatologia.org/archives/rmf\_28\_1\_articulo\_1.pdf},
  volume       = {28},
  year         = {2010},
}

Chicago
Slabbinck, Bram, Bernard De Baets, Peter Dawyndt, and Paul De Vos. 2010. “Análisis De Pseudomonas Fitopatógenas Usando Métodos Inteligentes De Aprendizaje Un Enfoque General Sobre Taxonomía y Análisis De Ácidos Grasos Dentro Del Género Pseudomonas.” Revista Mexicana De Fitopatologia 28 (1).
APA
Slabbinck, B., De Baets, B., Dawyndt, P., & De Vos, P. (2010). Análisis de Pseudomonas fitopatógenas usando métodos inteligentes de aprendizaje un enfoque general sobre taxonomía y análisis de ácidos grasos dentro del género Pseudomonas. REVISTA MEXICANA DE FITOPATOLOGIA, 28(1).
Vancouver
1.
Slabbinck B, De Baets B, Dawyndt P, De Vos P. Análisis de Pseudomonas fitopatógenas usando métodos inteligentes de aprendizaje un enfoque general sobre taxonomía y análisis de ácidos grasos dentro del género Pseudomonas. REVISTA MEXICANA DE FITOPATOLOGIA. 2010;28(1).
MLA
Slabbinck, Bram, Bernard De Baets, Peter Dawyndt, et al. “Análisis De Pseudomonas Fitopatógenas Usando Métodos Inteligentes De Aprendizaje Un Enfoque General Sobre Taxonomía y Análisis De Ácidos Grasos Dentro Del Género Pseudomonas.” REVISTA MEXICANA DE FITOPATOLOGIA 28.1 (2010): n. pag. Print.