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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)
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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.
Keywords
non-pathogenic bacteria, Diagnosis

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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, Bram, 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.
@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},
}