Arabidopsis thaliana computationally-generated next-state gene interaction models

Descripción del Articulo

The construction of gene interaction models must be a fully collaborative and intentional effort. All aspects of the research, such as growing the plants, extracting the measurements, refining the measured data, developing the statistical framework, and forming and applying the algorithmic technique...

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Detalles Bibliográficos
Autores: LaPointe, Bree, John, David, Norris, James, Harkey, Alexandria, Muhlemann, Joëlle, Muday, Gloria
Formato: objeto de conferencia
Fecha de Publicación:2019
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/8739
Enlace del recurso:https://hdl.handle.net/20.500.12724/8739
Nivel de acceso:acceso abierto
Materia:Redes neuronales artificiales
Arabidopsis thaliana
Artificial neural networks
Genetic transcription
Transcripción genética
Ingeniería de sistemas / Diseño y métodos
http://purl.org/pe-repo/ocde/ford#2.02.04
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dc.title.en.fl_str_mv Arabidopsis thaliana computationally-generated next-state gene interaction models
title Arabidopsis thaliana computationally-generated next-state gene interaction models
spellingShingle Arabidopsis thaliana computationally-generated next-state gene interaction models
LaPointe, Bree
Redes neuronales artificiales
Arabidopsis thaliana
Artificial neural networks
Genetic transcription
Transcripción genética
Ingeniería de sistemas / Diseño y métodos
http://purl.org/pe-repo/ocde/ford#2.02.04
title_short Arabidopsis thaliana computationally-generated next-state gene interaction models
title_full Arabidopsis thaliana computationally-generated next-state gene interaction models
title_fullStr Arabidopsis thaliana computationally-generated next-state gene interaction models
title_full_unstemmed Arabidopsis thaliana computationally-generated next-state gene interaction models
title_sort Arabidopsis thaliana computationally-generated next-state gene interaction models
author LaPointe, Bree
author_facet LaPointe, Bree
John, David
Norris, James
Harkey, Alexandria
Muhlemann, Joëlle
Muday, Gloria
author_role author
author2 John, David
Norris, James
Harkey, Alexandria
Muhlemann, Joëlle
Muday, Gloria
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv LaPointe, Bree
John, David
Norris, James
Harkey, Alexandria
Muhlemann, Joëlle
Muday, Gloria
dc.subject.es.fl_str_mv Redes neuronales artificiales
topic Redes neuronales artificiales
Arabidopsis thaliana
Artificial neural networks
Genetic transcription
Transcripción genética
Ingeniería de sistemas / Diseño y métodos
http://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.en_EN.fl_str_mv Arabidopsis thaliana
Artificial neural networks
Genetic transcription
dc.subject.es_PE.fl_str_mv Transcripción genética
dc.subject.classification.es.fl_str_mv Ingeniería de sistemas / Diseño y métodos
dc.subject.ocde.es_PE.fl_str_mv http://purl.org/pe-repo/ocde/ford#2.02.04
description The construction of gene interaction models must be a fully collaborative and intentional effort. All aspects of the research, such as growing the plants, extracting the measurements, refining the measured data, developing the statistical framework, and forming and applying the algorithmic techniques, must lend themselves to repeatable and sound practices. This paper holistically focuses on the process of producing gene interaction models based on transcript abundance data from Arabidopsis thaliana after stimulation by a plant hormone.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-07-09T21:33:15Z
dc.date.available.none.fl_str_mv 2019-07-09T21:33:15Z
dc.date.issued.fl_str_mv 2019
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/conferenceObject
dc.type.other.es_PE.fl_str_mv Artículo de conferencia
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dc.identifier.citation.es.fl_str_mv LaPointe, B., John, D., Norris, J., Harkey, A. F., Muhlemann, J. K. & Muday, G. K. (2019). Arabidopsis thaliana computationally-generated next-state gene interaction models. En Hacia la transformación digital. Actas del I Congreso Internacional de Ingeniería de Sistemas (pp. 17-26). Lima, 13 y 14 de septiembre del 2018. Universidad de Lima, Fondo Editorial.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12724/8739
identifier_str_mv LaPointe, B., John, D., Norris, J., Harkey, A. F., Muhlemann, J. K. & Muday, G. K. (2019). Arabidopsis thaliana computationally-generated next-state gene interaction models. En Hacia la transformación digital. Actas del I Congreso Internacional de Ingeniería de Sistemas (pp. 17-26). Lima, 13 y 14 de septiembre del 2018. Universidad de Lima, Fondo Editorial.
url https://hdl.handle.net/20.500.12724/8739
dc.language.iso.es_ES.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.none.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv Universidad de Lima, Fondo Editorial
dc.publisher.country.none.fl_str_mv PE
dc.source.es_PE.fl_str_mv Universidad de Lima
Repositorio Institucional - Ulima
dc.source.none.fl_str_mv reponame:ULIMA-Institucional
instname:Universidad de Lima
instacron:ULIMA
instname_str Universidad de Lima
instacron_str ULIMA
institution ULIMA
reponame_str ULIMA-Institucional
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spelling LaPointe, BreeJohn, DavidNorris, JamesHarkey, AlexandriaMuhlemann, JoëlleMuday, Gloria2019-07-09T21:33:15Z2019-07-09T21:33:15Z2019LaPointe, B., John, D., Norris, J., Harkey, A. F., Muhlemann, J. K. & Muday, G. K. (2019). Arabidopsis thaliana computationally-generated next-state gene interaction models. En Hacia la transformación digital. Actas del I Congreso Internacional de Ingeniería de Sistemas (pp. 17-26). Lima, 13 y 14 de septiembre del 2018. Universidad de Lima, Fondo Editorial.https://hdl.handle.net/20.500.12724/8739The construction of gene interaction models must be a fully collaborative and intentional effort. All aspects of the research, such as growing the plants, extracting the measurements, refining the measured data, developing the statistical framework, and forming and applying the algorithmic techniques, must lend themselves to repeatable and sound practices. 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Arabidopsis thaliana computationally-generated next-state gene interaction models
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