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...
- Autores:
-
Bedoya, O. (Óscar)
Bustamante, S. (Santiago)
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2011
- Institución:
- Universidad EIA .
- Repositorio:
- Repositorio EIA .
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eia.edu.co:11190/165
- Acceso en línea:
- https://repository.eia.edu.co/handle/11190/165
- Palabra clave:
- REI00157
PERCEPTRONES
PERCEPTRONS
INTELIGENCIA ARTIFICIAL
ARTIFICIAL INTELLIGENCE
TECNOLOGÍAS PARA LA SALUD
TECHNOLOGY IN HEALTH
PROMOTER PREDICTION
NEURAL NETWORKS
CONSENSUS STRATEGY
PREDICCIÓN DE PROMOTORES
REDES NEURONALES
ESTRATEGIA DE CONSENSO
- Rights
- openAccess
- License
- Derechos Reservados - Universidad EIA, 2020
| id |
REIA2_097df72f79241bde410c3ff75ada6743 |
|---|---|
| oai_identifier_str |
oai:repository.eia.edu.co:11190/165 |
| 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.alternative.spa.fl_str_mv |
CNN-Promoter, novo programa para a predição de promotores baseado em redes neuronais CNN-Promoter, nuevo programa para la predicción de promotores basado en redes neuronales |
| title |
CNN-Promoter, new consensus promoter prediction program based on neural networks |
| spellingShingle |
CNN-Promoter, new consensus promoter prediction program based on neural networks REI00157 PERCEPTRONES PERCEPTRONS INTELIGENCIA ARTIFICIAL ARTIFICIAL INTELLIGENCE TECNOLOGÍAS PARA LA SALUD TECHNOLOGY IN HEALTH PROMOTER PREDICTION NEURAL NETWORKS CONSENSUS STRATEGY 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, O. (Óscar) Bustamante, S. (Santiago) |
| dc.contributor.author.spa.fl_str_mv |
Bedoya, O. (Óscar) Bustamante, S. (Santiago) |
| dc.subject.lcsh.spa.fl_str_mv |
REI00157 |
| topic |
REI00157 PERCEPTRONES PERCEPTRONS INTELIGENCIA ARTIFICIAL ARTIFICIAL INTELLIGENCE TECNOLOGÍAS PARA LA SALUD TECHNOLOGY IN HEALTH PROMOTER PREDICTION NEURAL NETWORKS CONSENSUS STRATEGY PREDICCIÓN DE PROMOTORES REDES NEURONALES ESTRATEGIA DE CONSENSO |
| dc.subject.arcmarc.spa.fl_str_mv |
PERCEPTRONES PERCEPTRONS INTELIGENCIA ARTIFICIAL ARTIFICIAL INTELLIGENCE |
| dc.subject.eia.spa.fl_str_mv |
TECNOLOGÍAS PARA LA SALUD TECHNOLOGY IN HEALTH |
| dc.subject.keywords.spa.fl_str_mv |
PROMOTER PREDICTION NEURAL NETWORKS CONSENSUS STRATEGY 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 %. |
| publishDate |
2011 |
| dc.date.created.spa.fl_str_mv |
2011-07 |
| dc.date.submitted.spa.fl_str_mv |
2011-02-24 |
| dc.date.accepted.spa.fl_str_mv |
2011-06-23 |
| dc.date.accessioned.spa.fl_str_mv |
2013-11-19T13:43:20Z |
| dc.date.available.spa.fl_str_mv |
2013-11-19T13:43:20Z |
| dc.date.issued.spa.fl_str_mv |
2013-11-19 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
https://purl.org/redcol/resource_type/ART |
| dc.type.coarversion.spa.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.spa.fl_str_mv |
ISSN 17941237 |
| dc.identifier.uri.spa.fl_str_mv |
https://repository.eia.edu.co/handle/11190/165 |
| dc.identifier.bibliographiccitation.spa.fl_str_mv |
Bedoya, O., and Bustamante, S. (2011). CNN-Promoter, new consensus promoter prediction program based on neural networks, Revista EIA, 8 (15), 153-164. doi: http://hdl.handle.net/11190/165 |
| identifier_str_mv |
ISSN 17941237 Bedoya, O., and Bustamante, S. (2011). CNN-Promoter, new consensus promoter prediction program based on neural networks, Revista EIA, 8 (15), 153-164. doi: http://hdl.handle.net/11190/165 |
| url |
https://repository.eia.edu.co/handle/11190/165 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.spa.fl_str_mv |
Revista EIA |
| dc.relation.references.spa.fl_str_mv |
Abeel, T.; Saeys, Y.; Bonnet, E.; Rouzé, P. and Van de Peer, Y. (2008). “Generic eukaryotic core promoter prediction using structural features of DNA”. Genome Research, vol. 18, No. 2 (February), pp. 310-323. Allen, J. E.; Pertea, M. and Salzberg, S. L. (2004). “Computational gene prediction using multiple sources of evidence”. Genome Research, vol. 14, No. 1 (January), pp. 142-148. Bajic, V.; Seah, S.; Chong, A; Zhang, G; Koh, J. L. Y. and Brusic, V. (2002). “Dragon Promoter Finder: Recognition of vertebrate RNA polymerase II promoters”. Bioinformatics, vol. 18, No. 1 (January), pp. 198-199. Barlow, T. W. Feed-forward neural networks for secondary structure prediction. (1995). Journal of Molecular Graphics and Modelling, vol. 13, No. 3 (June), pp.175-183. Burden, S.; Lin, Y.-X. and Zhang, R. (2005). “Improving promoter prediction for the NNPP2.2 algorithm: A case study using Escherichia coli DNA sequences”. Bioinformatics, vol. 21, No. 5 (March), pp. 601-607. |
| dc.rights.spa.fl_str_mv |
Derechos Reservados - Universidad EIA, 2020 |
| dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.creativecommons.spa.fl_str_mv |
Atribución-NoComercial |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Derechos Reservados - Universidad EIA, 2020 https://creativecommons.org/licenses/by-nc/4.0/ Atribución-NoComercial http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
12 p. |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.editor.spa.fl_str_mv |
Fondo Editorial EIA |
| institution |
Universidad EIA . |
| bitstream.url.fl_str_mv |
https://repository.eia.edu.co/bitstreams/d4076974-1dce-4f45-96a6-74067aa072c9/download https://repository.eia.edu.co/bitstreams/2d0fdaa8-982e-4714-a78a-49f179fa4c9d/download https://repository.eia.edu.co/bitstreams/3ee06374-3535-4a78-a615-a66071632347/download https://repository.eia.edu.co/bitstreams/b51b9be3-4f84-480d-a9a7-9ed71d8b9fe2/download |
| bitstream.checksum.fl_str_mv |
2ff67ce87d1576f38c5cb4da616291ee dbab686e6b7233377fdbec667d30dbae 66874b0b9366b748c60895d2fb6339f8 141e8bc5e1e9f82bc69d111ff8002613 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad EIA |
| repository.mail.fl_str_mv |
bdigital@metabiblioteca.com |
| _version_ |
1814100877604028416 |
| spelling |
Bedoya, O. (Óscar)4371022165a2ff0147783c7a5daed799-1Bustamante, S. (Santiago)6f963a05b2b31ff58a20405c4e60c5d9-1oscar.bedoya@correounivalle.edu.cosbustam@univalle.edu.co2013-11-19T13:43:20Z2013-11-19T13:43:20Z2011-072013-11-192011-02-242011-06-23ISSN 17941237https://repository.eia.edu.co/handle/11190/165Bedoya, O., and Bustamante, S. (2011). CNN-Promoter, new consensus promoter prediction program based on neural networks, Revista EIA, 8 (15), 153-164. doi: http://hdl.handle.net/11190/165A 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 %.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.12 p.application/pdfengRevista EIAAbeel, T.; Saeys, Y.; Bonnet, E.; Rouzé, P. and Van de Peer, Y. (2008). “Generic eukaryotic core promoter prediction using structural features of DNA”. Genome Research, vol. 18, No. 2 (February), pp. 310-323.Allen, J. E.; Pertea, M. and Salzberg, S. L. (2004). “Computational gene prediction using multiple sources of evidence”. Genome Research, vol. 14, No. 1 (January), pp. 142-148.Bajic, V.; Seah, S.; Chong, A; Zhang, G; Koh, J. L. Y. and Brusic, V. (2002). “Dragon Promoter Finder: Recognition of vertebrate RNA polymerase II promoters”. Bioinformatics, vol. 18, No. 1 (January), pp. 198-199.Barlow, T. W. Feed-forward neural networks for secondary structure prediction. (1995). Journal of Molecular Graphics and Modelling, vol. 13, No. 3 (June), pp.175-183.Burden, S.; Lin, Y.-X. and Zhang, R. (2005). “Improving promoter prediction for the NNPP2.2 algorithm: A case study using Escherichia coli DNA sequences”. Bioinformatics, vol. 21, No. 5 (March), pp. 601-607.Derechos Reservados - Universidad EIA, 2020https://creativecommons.org/licenses/by-nc/4.0/El autor de la obra, actuando en nombre propio, hace entrega del ejemplar respectivo y de sus anexos en formato digital o electrónico y autoriza a la ESCUELA DE INGENIERIA DE ANTIOQUIA, para que en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión andina 351 de 1993, Decreto 460 de 1995, y demás normas generales sobre la materia, utilice y use por cualquier medio conocido o por conocer, los derechos patrimoniales de reproducción, comunicación pública, transformación y distribución de la obra objeto del presente documento. PARÁGRAFO: La presente autorización se hace extensiva no sólo a las dependencias y derechos de uso sobre la obra en formato o soporte material, sino también para formato virtual, electrónico, digital, y en red, internet, extranet, intranet, etc., y en general en cualquier formato conocido o por conocer. EL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realiza sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. PARÁGRAFO: En caso de presentarse cualquier reclamación o acción por parte de un tercero en cuanto a los derechos de autor sobre la obra en cuestión, EL AUTOR, asumirá toda la responsabilidad, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos la ESCUELA DE INGENIERÍA DE ANTIOQUIA actúa como un tercero de buena fe.info:eu-repo/semantics/openAccessAtribución-NoComercialhttp://purl.org/coar/access_right/c_abf2REI00157PERCEPTRONESPERCEPTRONSINTELIGENCIA ARTIFICIALARTIFICIAL INTELLIGENCETECNOLOGÍAS PARA LA SALUDTECHNOLOGY IN HEALTHPROMOTER PREDICTIONNEURAL NETWORKSCONSENSUS STRATEGYPREDICCIÓN DE PROMOTORESREDES NEURONALESESTRATEGIA DE CONSENSOCNN-Promoter, new consensus promoter prediction program based on neural networksCNN-Promoter, novo programa para a predição de promotores baseado em redes neuronaisCNN-Promoter, nuevo programa para la predicción de promotores basado en redes neuronalesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttps://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Fondo Editorial EIAPublicationTHUMBNAILREI00157.pdf.jpgREI00157.pdf.jpgGenerated Thumbnailimage/jpeg11646https://repository.eia.edu.co/bitstreams/d4076974-1dce-4f45-96a6-74067aa072c9/download2ff67ce87d1576f38c5cb4da616291eeMD54ORIGINALREI00157.pdfREI00157.pdfapplication/pdf1118721https://repository.eia.edu.co/bitstreams/2d0fdaa8-982e-4714-a78a-49f179fa4c9d/downloaddbab686e6b7233377fdbec667d30dbaeMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81494https://repository.eia.edu.co/bitstreams/3ee06374-3535-4a78-a615-a66071632347/download66874b0b9366b748c60895d2fb6339f8MD52TEXTREI00157.pdf.txtREI00157.pdf.txtExtracted texttext/plain37991https://repository.eia.edu.co/bitstreams/b51b9be3-4f84-480d-a9a7-9ed71d8b9fe2/download141e8bc5e1e9f82bc69d111ff8002613MD5311190/165oai:repository.eia.edu.co:11190/1652023-07-25 16:48:30.149https://creativecommons.org/licenses/by-nc/4.0/Derechos Reservados - Universidad EIA, 2020open.accesshttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.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 |