Modelo basado en técnicas de machine learning para la clasificación de virus de ARN

ilustraciones, diagramas

Autores:
Colmenares Celis, Carolina
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84608
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84608
https://repositorio.unal.edu.co/
Palabra clave:
Transmisión de enfermedad infecciosa
ARN viral
Zoonosis virales
Disease Transmission, Infectious
Viral Zoonoses
RNA, Viral
Virus ARN
Metagenómica
Metavirómica
Aprendizaje de máquina
Estructuras secundarias
Clasificación
RNA viruses
Metagenomics
Metaviromics
Machine learning
Secondary structures
Classification
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_0a71cef042da045f3aad315d40ea99ca
oai_identifier_str oai:repositorio.unal.edu.co:unal/84608
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
dc.title.translated.eng.fl_str_mv Model based on machine learning techniques for the classification of RNA viruses
title Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
spellingShingle Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
Transmisión de enfermedad infecciosa
ARN viral
Zoonosis virales
Disease Transmission, Infectious
Viral Zoonoses
RNA, Viral
Virus ARN
Metagenómica
Metavirómica
Aprendizaje de máquina
Estructuras secundarias
Clasificación
RNA viruses
Metagenomics
Metaviromics
Machine learning
Secondary structures
Classification
title_short Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
title_full Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
title_fullStr Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
title_full_unstemmed Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
title_sort Modelo basado en técnicas de machine learning para la clasificación de virus de ARN
dc.creator.fl_str_mv Colmenares Celis, Carolina
dc.contributor.advisor.none.fl_str_mv Bermúdez Santana, Clara Isabel
Niño Vásquez, Luis Fernando
dc.contributor.author.none.fl_str_mv Colmenares Celis, Carolina
dc.contributor.researchgroup.spa.fl_str_mv Rnomica Teórica y Computacional
laboratorio de Investigación en Sistemas Inteligentes Lisi
dc.subject.decs.spa.fl_str_mv Transmisión de enfermedad infecciosa
ARN viral
Zoonosis virales
topic Transmisión de enfermedad infecciosa
ARN viral
Zoonosis virales
Disease Transmission, Infectious
Viral Zoonoses
RNA, Viral
Virus ARN
Metagenómica
Metavirómica
Aprendizaje de máquina
Estructuras secundarias
Clasificación
RNA viruses
Metagenomics
Metaviromics
Machine learning
Secondary structures
Classification
dc.subject.decs.eng.fl_str_mv Disease Transmission, Infectious
Viral Zoonoses
dc.subject.lemb.spa.fl_str_mv RNA, Viral
dc.subject.proposal.spa.fl_str_mv Virus ARN
Metagenómica
Metavirómica
Aprendizaje de máquina
Estructuras secundarias
Clasificación
dc.subject.proposal.eng.fl_str_mv RNA viruses
Metagenomics
Metaviromics
Machine learning
Secondary structures
Classification
description ilustraciones, diagramas
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-29T14:07:59Z
dc.date.available.none.fl_str_mv 2023-08-29T14:07:59Z
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/84608
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/84608
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bermúdez Santana, Clara Isabel4640436fa6ecd6a7d3ab0cad7b367eaeNiño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbeColmenares Celis, Carolina4205c0dd28645d4e6d82371d4b52e857Rnomica Teórica y Computacionallaboratorio de Investigación en Sistemas Inteligentes Lisi2023-08-29T14:07:59Z2023-08-29T14:07:59Z2023https://repositorio.unal.edu.co/handle/unal/84608Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLos virus son las entidades biológicas más abundantes de la Tierra, pero detectarlos, aislarlos y clasificarlos ha sido todo un reto para la ciencia. Los virus de ARN patógenos causan numerosas muertes humanas, especialmente los implicados en la transmisión de enfermedades zoonóticas, lo que conduce a emergencias víricas y pandemias globales como la asociada al SARS-CoV-2. En este estudio, se explora y describen representaciones teóricas como la de árbol extendido, HIT y árbol de grano grueso para virus de ARN, basados en niveles de secuencia y estructura. Estas representaciones se utilizaron para determinar cuál de ellas demuestra un mejor potencial como entradas para un modelo de clasificación basado en técnicas de aprendizaje de máquina. Para el diseño del modelo, se investigaron algoritmos de perceptrón multicapa, árboles de sufijos, modelos ocultos de Markov (HMM) y redes neuronales convolucionales con memoria de corto y largo plazo (CNN-LSTM). La aplicación de estos algoritmos se llevó a cabo utilizando dos conjuntos de datos. Los datos de entrenamiento consistieron en secuencias de familias de virus ARN, incluyendo Orthomyxoviridae, Sedoreoviridae, Spinareoviridae, Retroviridae y Arteriviridae, obtenidas de la base de datos del Centro Nacional para la Información Biotecnológica (NCBI). Los datos de prueba están comprendidos de metaviromas recolectados durante la "Expedición Biológica en Ecosistemas Representativos de Colombia: Bosque húmedo tropical de la Sierra Nevada de Santa Marta", un proyecto financiado por Colciencias en colaboración con el grupo de investigación teórica y computacional RNomica de la Universidad Nacional de Colombia. Ambos conjuntos de datos se transformaron en las representaciones estructurales mencionadas utilizando el paquete ViennaRNA. La representación HIT mostró las mejores características para la extracción, y los modelos basados en HMMs y CNN-LSTM demostraron un rendimiento superior y potencial para clasificar metagenomas de virus ARN. (Texto tomado de la fuente)Viruses are the most abundant biological entities on Earth, but detecting, isolating, and classifying them has posed a significant challenge for science. Pathogenic RNA viruses cause numerous human deaths, especially those involved in the transmission of zoonotic diseases, leading to viral emergencies and global pandemics like the one associated with SARS-CoV-2. In this study, theoretical frameworks such as extended tree, HIT, and coarse-grained tree are explored and described for RNA viruses, based on levels of sequence and structure. These representations were used to determine which of them demonstrates better potential as inputs for a classification model based on machine learning techniques. For model design, algorithms including multilayer perceptrons, suffix trees, hidden Markov models (HMMs), and convolutional neural networks with short and long-term memory (CNN-LSTM) were investigated. The application of these algorithms was carried out using two datasets. The training data consisted of sequences from families of RNA viruses, including Orthomyxoviridae, Sedoreoviridae, Spinareoviridae, Retroviridae, and Arteriviridae, obtained from the National Center for Biotechnology Information (NCBI) database. The test data comprised metaviromes collected during the "Biological Expedition in Representative Ecosystems of Colombia: Tropical Rainforest of the Sierra Nevada de Santa Marta," a project funded by Colciencias in collaboration with the theoretical and computational research group RNomica at the National University of Colombia. Both datasets were transformed into the mentioned structural representations using the ViennaRNA package. The HIT representation exhibited the most favorable features for extraction, and models based on HMMs and CNN-LSTM demonstrated superior performance and potential for classifying RNA virus metagenomes.MaestríaTecnologías computacionales en Bioinformática114 páginosapplication/pdfspaModelo basado en técnicas de machine learning para la clasificación de virus de ARNModel based on machine learning techniques for the classification of RNA virusesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBogotá - Ingeniería - Maestría en BioinformáticaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede BogotáMarz M, Beerenwinkel N, Drosten C, et al. (2014) Challenges in RNA virus bioinformatics.30(13):1793-1799. doi:10.1093/bioinformatics/btu105Villa, T.G., Abril, A.G., Sanchez, S. et al. Animal and human RNA viruses: genetic variability and ability to overcome vaccines. 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Methods in microbiology, 44, 19-35.Transmisión de enfermedad infecciosaARN viralZoonosis viralesDisease Transmission, InfectiousViral ZoonosesRNA, ViralVirus ARNMetagenómicaMetavirómicaAprendizaje de máquinaEstructuras secundariasClasificaciónRNA virusesMetagenomicsMetaviromicsMachine learningSecondary structuresClassificationORIGINAL1020808077.2023.pdf1020808077.2023.pdfTesis de Maestría en Bioinformáticaapplication/pdf3065650https://repositorio.unal.edu.co/bitstream/unal/84608/4/1020808077.2023.pdf3b3f4f66744d9cd102bd27b1e89964b2MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84608/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1020808077.2023.pdf.jpg1020808077.2023.pdf.jpgGenerated Thumbnailimage/jpeg3684https://repositorio.unal.edu.co/bitstream/unal/84608/5/1020808077.2023.pdf.jpgb3316e619df562868fdb783a03a92e94MD55unal/84608oai:repositorio.unal.edu.co:unal/846082024-08-11 01:06:54.527Repositorio Institucional 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