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...

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Autores principales:	Gürüler,Hüseyin, Peker,Musa, Baysal,Ömür
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Valparaíso 2015
Materias:	Computational biology Genetic diversity Molecular markers Plant pathogens Predictive information Soft computing
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000500004
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spelling	oai:scielo:S0717-345820150005000042016-03-21Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approachGürüler,HüseyinPeker,MusaBaysal,Ömür Computational biology Genetic diversity Molecular markers Plant pathogens Predictive information Soft computing 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 presence/absence of certain sequence markers as predictive features. Results A plant pathogen, causing downy mildew disease on cucurbits was considered as a model microorganism. Significant accuracy was achieved with particle swarm optimization (PSO) trained ANNs. Conclusions This pioneer study for estimation of pathogen properties using molecular markers demonstrates that neural networks achieve good performance for the proposed application.info:eu-repo/semantics/openAccessPontificia Universidad Católica de ValparaísoElectronic Journal of Biotechnology v.18 n.5 20152015-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000500004en10.1016/j.ejbt.2015.06.006
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Computational biology Genetic diversity Molecular markers Plant pathogens Predictive information Soft computing
spellingShingle	Computational biology Genetic diversity Molecular markers Plant pathogens Predictive information Soft computing Gürüler,Hüseyin Peker,Musa Baysal,Ömür Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
description	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 presence/absence of certain sequence markers as predictive features. Results A plant pathogen, causing downy mildew disease on cucurbits was considered as a model microorganism. Significant accuracy was achieved with particle swarm optimization (PSO) trained ANNs. Conclusions This pioneer study for estimation of pathogen properties using molecular markers demonstrates that neural networks achieve good performance for the proposed application.
author	Gürüler,Hüseyin Peker,Musa Baysal,Ömür
author_facet	Gürüler,Hüseyin Peker,Musa Baysal,Ömür
author_sort	Gürüler,Hüseyin
title	Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
title_short	Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
title_full	Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
title_fullStr	Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
title_full_unstemmed	Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach
title_sort	soft computing model on genetic diversity and pathotype differentiation of pathogens: a novel approach
publisher	Pontificia Universidad Católica de Valparaíso
publishDate	2015
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582015000500004
work_keys_str_mv	AT gurulerhuseyin softcomputingmodelongeneticdiversityandpathotypedifferentiationofpathogensanovelapproach AT pekermusa softcomputingmodelongeneticdiversityandpathotypedifferentiationofpathogensanovelapproach AT baysalomur softcomputingmodelongeneticdiversityandpathotypedifferentiationofpathogensanovelapproach
_version_	1718441915927494656

Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach

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