Electronic Journal of Biotechnology
Universidad Católica de Valparaíso
Vol. 18, No. 5, 2015, pp. 347-354
Bioline Code: ej15058
Full paper language: English
Document type: Research Article
Document available free of charge
Electronic Journal of Biotechnology, Vol. 18, No. 5, 2015, pp. 347-354
© Copyright 2015 - Electronic Journal of Biotechnology
Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach|
Gürüler, Hüseyin; Peker, Musa & Baysal, Ömür
Identifying and validating biomarkers' scores of polymorphic bands are important for studies related
to the molecular diversity of pathogens. Although these validations provide more relevant results, the
experiments are very complex and time-consuming. Besides rapid identification of plant pathogens causing
disease, assessing genetic diversity and pathotype formation using automated soft computing methods are
advantageous in terms of following genetic variation of pathogens on plants. In the present study, artificial
neural network (ANN) as a soft computing method was applied to classify plant pathogen types and fungicide
susceptibilities using the presenceabsence of certain sequence markers as predictive features.
A plant pathogen, causing downy mildewdisease on cucurbitswas considered as amodelmicroorganism.
Significant accuracy was achieved with particle swarm optimization (PSO) trained ANNs.
This pioneer study for estimation of pathogen properties using molecularmarkers demonstrates that
neural networks achieve good performance for the proposed application.
Computational biology; Genetic diversity; Molecular markers; Plant pathogens; Predictive information; Soft computing
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