Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas

Ilustraciones

Autores:
Orrego Pérez, Andrés
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/83835
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/83835
https://repositorio.unal.edu.co/
Palabra clave:
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Redes neuronales
Péptidos
GAN
Antimicrobial peptides
Deep learning
Sequence generation
GAN
Péptidos antimicrobianos
Aprendizaje profundo
Generación de secuencias
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_594932a1200d8c95c3621998e6500dc2
oai_identifier_str oai:repositorio.unal.edu.co:unal/83835
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
dc.title.translated.eng.fl_str_mv Deep Learning Model for automatic generation of synthetic antimicrobials peptides with specific functionalities
title Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
spellingShingle Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Redes neuronales
Péptidos
GAN
Antimicrobial peptides
Deep learning
Sequence generation
GAN
Péptidos antimicrobianos
Aprendizaje profundo
Generación de secuencias
title_short Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
title_full Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
title_fullStr Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
title_full_unstemmed Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
title_sort Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
dc.creator.fl_str_mv Orrego Pérez, Andrés
dc.contributor.advisor.none.fl_str_mv Mera Banguero, Carlos Andres
Branch Bedoya, John Willian
Orduz Peralta, Sergio
dc.contributor.author.none.fl_str_mv Orrego Pérez, Andrés
dc.contributor.researchgroup.spa.fl_str_mv Biología Funcional
Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.contributor.orcid.spa.fl_str_mv 0000-0002-5143-0276
dc.contributor.cvlac.spa.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001737101
dc.contributor.googlescholar.spa.fl_str_mv https://scholar.google.com/citations?user=K6Tz_4QAAAAJ&hl=es
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Redes neuronales
Péptidos
GAN
Antimicrobial peptides
Deep learning
Sequence generation
GAN
Péptidos antimicrobianos
Aprendizaje profundo
Generación de secuencias
dc.subject.armarc.none.fl_str_mv Redes neuronales
dc.subject.lemb.none.fl_str_mv Péptidos
dc.subject.proposal.eng.fl_str_mv GAN
Antimicrobial peptides
Deep learning
Sequence generation
dc.subject.proposal.spa.fl_str_mv GAN
Péptidos antimicrobianos
Aprendizaje profundo
Generación de secuencias
description Ilustraciones
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-05-19T19:12:22Z
dc.date.available.none.fl_str_mv 2023-05-19T19:12:22Z
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/83835
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/83835
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
dc.relation.indexed.spa.fl_str_mv LaReferencia
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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_abf2Mera Banguero, Carlos Andres6389e3503770a49b50672c151957265fBranch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15600Orduz Peralta, Sergiobfc3c74726167d38ddd831c691a9e32f600Orrego Pérez, Andrése449eb91df1f9b6150c0dc286d820343Biología FuncionalGidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial0000-0002-5143-0276https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001737101https://scholar.google.com/citations?user=K6Tz_4QAAAAJ&hl=es2023-05-19T19:12:22Z2023-05-19T19:12:22Z2023https://repositorio.unal.edu.co/handle/unal/83835Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesLa resistencia a los antibióticos se ha convertido en uno de los mayores problemas de salud a nivel mundial en los últimos años, provocando afectaciones directas contra la salud y la economía. Un tipo especial de proteínas cortas, denominadas péptidos antimicrobianos, está tomando gran relevancia en la investigación para combatir esta problemática, principalmente por sus bondades antibióticas. Existen diferentes métodos para la búsqueda de nuevos péptidos antimicrobianos, entre ellos está el uso de técnicas de aprendizaje automático que permiten reducir los costos y el tiempo de búsqueda, comparadas con las técnicas tradicionales de bioprospección. En esa línea, en este trabajo se propone un método para la generación de secuencias sintéticas de péptidos antimicrobianos con funcionalidades específicas utilizando una red neuronal con una arquitectura GAN condicional y celdas recurrentes. Este método es evaluado a partir de una estrategia de validación propuesta que se enfoca en medir la calidad y diversidad de las secuencias sintéticas generadas. Los modelos obtenidos fueron comparados con algunas referencias del estado del arte y los resultados mostraron que las secuencias generadas por los modelos propuestos tienen alto potencial antimicrobiano, son diversas, estructuralmente distintas a las secuencias de entrenamiento, pero similares a nivel de su composición de aminoácidos. Adicionalmente, los modelos propuestos pueden generar, a petición del usuario, secuencias con las siguientes funcionalidades específicas: antimicrobiano, antibacteriano, anti gramnegativo, anti grampositivo, antifúngico, antiviral, y anticáncer. (Tomado de la fuente)Antibiotic resistance has become one of the biggest health problems worldwide in recent years, causing direct effects on health and the economy. A particular type of short protein, called antimicrobial peptides, is gaining great relevance in research to combat this problem, mainly due to its antibiotic benefits. There are different methods for searching for new antimicrobial peptide sequences, including machine learning techniques that reduce costs and search time compared to traditional bioprospecting techniques. In that line, this work proposes a method for generating synthetic sequences of antimicrobial peptides with specific functionalities using a neural network with a conditional GAN architecture and recurrent cells. This method is evaluated based on a proposed validation strategy that measures the quality and diversity of the generated synthetic sequences. The obtained models were compared with some state-of-the-art references. The results showed that the sequences generated by the proposed models have high antimicrobial potential and are diverse, structurally different from the training sequences, but similar at their amino acid composition level. Additionally, the proposed models can generate, at the user's request, sequences with the following specific functionalities: antimicrobial, antibacterial, anti-gram-negative, anti-gram-positive, antifungal, antiviral, and anticancer.MaestríaMagíster en Ingeniería - AnalíticaInteligencia ArtificialÁrea Curricular de Ingeniería de Sistemas e Informática65 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresRedes neuronalesPéptidosGANAntimicrobial peptidesDeep learningSequence generationGANPéptidos antimicrobianosAprendizaje profundoGeneración de secuenciasModelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicasDeep Learning Model for automatic generation of synthetic antimicrobials peptides with specific functionalitiesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferenciaDR. 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Horng, “Characterization and identification of antimicrobial peptides with different functional activities,” Brief Bioinform, vol. 21, no. 3, 2020, doi: 10.1093/bib/bbz043.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83835/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINALModelo de Aprendizaje Profundo para la Generación Automática de Péptidos Antimicrobianos Sintéticos con Funcionalidades Específicas.pdfModelo de Aprendizaje Profundo para la Generación Automática de Péptidos Antimicrobianos Sintéticos con Funcionalidades Específicas.pdfTesis de maestría en Ingeniería - Análiticaapplication/pdf1780832https://repositorio.unal.edu.co/bitstream/unal/83835/4/Modelo%20de%20Aprendizaje%20Profundo%20para%20la%20Generaci%c3%b3n%20Autom%c3%a1tica%20de%20P%c3%a9ptidos%20Antimicrobianos%20Sint%c3%a9ticos%20con%20Funcionalidades%20Espec%c3%adficas.pdf3e30c525de8fba0f0545cdb6e87654b2MD54THUMBNAILModelo de Aprendizaje 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