CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS

A new promoter prediction program called CNN-Promoter is presented. CNN-Promoter allows DNA sequences to be submitted and predicts them as promoter or non-promoter. Several methods have been developed to predict the promoter regions of genomes in eukaryotic organisms including algorithms based on Ma...

Full description

Autores:
Bedoya, Óscar
Bustamante, Santiago
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad EIA .
Repositorio:
Repositorio EIA .
Idioma:
eng
OAI Identifier:
oai:repository.eia.edu.co:11190/4754
Acceso en línea:
https://repository.eia.edu.co/handle/11190/4754
https://revistas.eia.edu.co/index.php/reveia/article/view/253
Palabra clave:
promoter prediction
neural networks
consensus strategy. Palabras clave
predicción de promotores
redes neuronales
estrategia de consenso.
Rights
openAccess
License
Revista EIA - 2013
id REIA2_d5aa35b4b73b5a922957b87c3bd3be5c
oai_identifier_str oai:repository.eia.edu.co:11190/4754
network_acronym_str REIA2
network_name_str Repositorio EIA .
repository_id_str
dc.title.spa.fl_str_mv CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
dc.title.translated.eng.fl_str_mv CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
title CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
spellingShingle CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
promoter prediction
neural networks
consensus strategy. Palabras clave
predicción de promotores
redes neuronales
estrategia de consenso.
title_short CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
title_full CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
title_fullStr CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
title_full_unstemmed CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
title_sort CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS
dc.creator.fl_str_mv Bedoya, Óscar
Bustamante, Santiago
dc.contributor.author.spa.fl_str_mv Bedoya, Óscar
Bustamante, Santiago
dc.subject.eng.fl_str_mv promoter prediction
neural networks
consensus strategy. Palabras clave
predicción de promotores
redes neuronales
estrategia de consenso.
topic promoter prediction
neural networks
consensus strategy. Palabras clave
predicción de promotores
redes neuronales
estrategia de consenso.
description A new promoter prediction program called CNN-Promoter is presented. CNN-Promoter allows DNA sequences to be submitted and predicts them as promoter or non-promoter. Several methods have been developed to predict the promoter regions of genomes in eukaryotic organisms including algorithms based on Markov’s models, decision trees, and statistical methods. Although there are plenty of programs proposed, there is still a need to improve the sensitivity and specificity values. In this paper, a new program is proposed; it is based on the consensus strategy of using experts to make a better prediction. The consensus strategy is developed by using neural networks. During the training process, the sensitivity and specificity were 100 % and during the test process the model reaches a sensitivity of 74.5 % and a specificity of 82.7 %.Abstract: En este artículo se presenta un programa nuevo para la predicción de promotores llamado CNN--Promoter, que toma como entrada secuencias de ADN y las clasifica como promotor o no promotor. Se han desarrollado diversos métodos para predecir las regiones promotoras en organismos eucariotas, muchos de los cuales se basan en modelos de Markov, árboles de decisión y métodos estadísticos. A pesar de la variedad de programas existentes para la predicción de promotores, se necesita aún mejorar los valores de sensibilidad y especificidad. Se propone un nuevo programa que se basa en la estrategia de mezcla de expertos usando redes neuronales. Los resultados obtenidos en las pruebas alcanzan valores de sensibilidad y especificidad de 100 % en el entrenamiento y de 74,5 % de sensibilidad y 82,7 % de especificidad en los conjuntos de validación y prueba.
publishDate 2013
dc.date.accessioned.none.fl_str_mv 2013-10-01 00:00:00
2022-06-17T20:16:44Z
dc.date.available.none.fl_str_mv 2013-10-01 00:00:00
2022-06-17T20:16:44Z
dc.date.issued.none.fl_str_mv 2013-10-01
dc.type.spa.fl_str_mv Artículo de revista
dc.type.eng.fl_str_mv Journal article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_6501
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.content.eng.fl_str_mv Text
dc.type.redcol.eng.fl_str_mv http://purl.org/redcol/resource_type/ARTREF
dc.type.coarversion.eng.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 1794-1237
dc.identifier.uri.none.fl_str_mv https://repository.eia.edu.co/handle/11190/4754
dc.identifier.eissn.none.fl_str_mv 2463-0950
dc.identifier.url.none.fl_str_mv https://revistas.eia.edu.co/index.php/reveia/article/view/253
identifier_str_mv 1794-1237
2463-0950
url https://repository.eia.edu.co/handle/11190/4754
https://revistas.eia.edu.co/index.php/reveia/article/view/253
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.bitstream.none.fl_str_mv https://revistas.eia.edu.co/index.php/reveia/article/download/253/248
dc.relation.citationedition.spa.fl_str_mv Núm. 15 , Año 2011
dc.relation.citationendpage.none.fl_str_mv 164
dc.relation.citationissue.spa.fl_str_mv 15
dc.relation.citationstartpage.none.fl_str_mv 153
dc.relation.citationvolume.spa.fl_str_mv 8
dc.relation.ispartofjournal.spa.fl_str_mv Revista EIA
dc.rights.eng.fl_str_mv Revista EIA - 2013
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.eng.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Revista EIA - 2013
https://creativecommons.org/licenses/by-nc-nd/4.0
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.eng.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Fondo Editorial EIA - Universidad EIA
dc.source.eng.fl_str_mv https://revistas.eia.edu.co/index.php/reveia/article/view/253
institution Universidad EIA .
bitstream.url.fl_str_mv https://repository.eia.edu.co/bitstreams/eeee9d5a-7c39-4ed5-9c55-670d21520a21/download
bitstream.checksum.fl_str_mv 907f44a5f0b706acabd152dc761e6663
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio Institucional Universidad EIA
repository.mail.fl_str_mv bdigital@metabiblioteca.com
_version_ 1814100885876244480
spelling Bedoya, Óscar32cc8692d071e3e1058731dc570b0bab300Bustamante, Santiago56b7767c8cc5577fae850c0a800f85ca3002013-10-01 00:00:002022-06-17T20:16:44Z2013-10-01 00:00:002022-06-17T20:16:44Z2013-10-011794-1237https://repository.eia.edu.co/handle/11190/47542463-0950https://revistas.eia.edu.co/index.php/reveia/article/view/253A new promoter prediction program called CNN-Promoter is presented. CNN-Promoter allows DNA sequences to be submitted and predicts them as promoter or non-promoter. Several methods have been developed to predict the promoter regions of genomes in eukaryotic organisms including algorithms based on Markov’s models, decision trees, and statistical methods. Although there are plenty of programs proposed, there is still a need to improve the sensitivity and specificity values. In this paper, a new program is proposed; it is based on the consensus strategy of using experts to make a better prediction. The consensus strategy is developed by using neural networks. During the training process, the sensitivity and specificity were 100 % and during the test process the model reaches a sensitivity of 74.5 % and a specificity of 82.7 %.Abstract: En este artículo se presenta un programa nuevo para la predicción de promotores llamado CNN--Promoter, que toma como entrada secuencias de ADN y las clasifica como promotor o no promotor. Se han desarrollado diversos métodos para predecir las regiones promotoras en organismos eucariotas, muchos de los cuales se basan en modelos de Markov, árboles de decisión y métodos estadísticos. A pesar de la variedad de programas existentes para la predicción de promotores, se necesita aún mejorar los valores de sensibilidad y especificidad. Se propone un nuevo programa que se basa en la estrategia de mezcla de expertos usando redes neuronales. Los resultados obtenidos en las pruebas alcanzan valores de sensibilidad y especificidad de 100 % en el entrenamiento y de 74,5 % de sensibilidad y 82,7 % de especificidad en los conjuntos de validación y prueba.A new promoter prediction program called CNN-Promoter is presented. CNN-Promoter allows DNA sequences to be submitted and predicts them as promoter or non-promoter. Several methods have been developed to predict the promoter regions of genomes in eukaryotic organisms including algorithms based on Markov’s models, decision trees, and statistical methods. Although there are plenty of programs proposed, there is still a need to improve the sensitivity and specificity values. In this paper, a new program is proposed; it is based on the consensus strategy of using experts to make a better prediction. The consensus strategy is developed by using neural networks. During the training process, the sensitivity and specificity were 100 % and during the test process the model reaches a sensitivity of 74.5 % and a specificity of 82.7 %.Abstract: En este artículo se presenta un programa nuevo para la predicción de promotores llamado CNN--Promoter, que toma como entrada secuencias de ADN y las clasifica como promotor o no promotor. Se han desarrollado diversos métodos para predecir las regiones promotoras en organismos eucariotas, muchos de los cuales se basan en modelos de Markov, árboles de decisión y métodos estadísticos. A pesar de la variedad de programas existentes para la predicción de promotores, se necesita aún mejorar los valores de sensibilidad y especificidad. Se propone un nuevo programa que se basa en la estrategia de mezcla de expertos usando redes neuronales. Los resultados obtenidos en las pruebas alcanzan valores de sensibilidad y especificidad de 100 % en el entrenamiento y de 74,5 % de sensibilidad y 82,7 % de especificidad en los conjuntos de validación y prueba.application/pdfengFondo Editorial EIA - Universidad EIARevista EIA - 2013https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/253promoter predictionneural networksconsensus strategy. Palabras clavepredicción de promotoresredes neuronalesestrategia de consenso.CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKSCNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKSArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://revistas.eia.edu.co/index.php/reveia/article/download/253/248Núm. 15 , Año 2011164151538Revista EIAPublicationOREORE.xmltext/xml2580https://repository.eia.edu.co/bitstreams/eeee9d5a-7c39-4ed5-9c55-670d21520a21/download907f44a5f0b706acabd152dc761e6663MD5111190/4754oai:repository.eia.edu.co:11190/47542023-07-25 16:59:03.569https://creativecommons.org/licenses/by-nc-nd/4.0Revista EIA - 2013metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com