1 research outputs found
Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
Background: 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.
Results: A plant pathogen, causing downy mildewdisease on cucurbitswas
considered as amodelmicroorganism. Significant accuracy was achieved
with particle swarm optimization (PSO) trained ANNs. Conclusions:
This pioneer study for estimation of pathogen properties using
molecularmarkers demonstrates that neural networks achieve good
performance for the proposed application