Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo

ilustraciones, diagramas

Autores:: Murcia Triviño, Jossie Esteban

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
dc.title.translated.eng.fl_str_mv	Machine learning-based epistasis model for intellectual disability and neurodevelopmental delay
title	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
spellingShingle	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 610 - Medicina y salud::616 - Enfermedades Epistasis Genética Personas con Discapacidades Mentales Epistasis, Genetic Persons with Mental Disabilities Epistasis Aprendizaje de máquinas Polimorfismo de un solo nucleótido Trastornos del neurodesarrollo Discapacidad intelectual Epistasis Machine learning Single nucleotide polymorphism Neurodevelopmental disorders Intellectual disability aprendizaje automático machine learning
title_short	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
title_full	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
title_fullStr	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
title_full_unstemmed	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
title_sort	Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo
dc.creator.fl_str_mv	Murcia Triviño, Jossie Esteban
dc.contributor.advisor.spa.fl_str_mv	Niño Vásquez, Luis Fernando López Rivera, Juan Javier
dc.contributor.author.spa.fl_str_mv	Murcia Triviño, Jossie Esteban
dc.contributor.researchgroup.spa.fl_str_mv	laboratorio de Investigación en Sistemas Inteligentes Lisi
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 610 - Medicina y salud::616 - Enfermedades
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 610 - Medicina y salud::616 - Enfermedades Epistasis Genética Personas con Discapacidades Mentales Epistasis, Genetic Persons with Mental Disabilities Epistasis Aprendizaje de máquinas Polimorfismo de un solo nucleótido Trastornos del neurodesarrollo Discapacidad intelectual Epistasis Machine learning Single nucleotide polymorphism Neurodevelopmental disorders Intellectual disability aprendizaje automático machine learning
dc.subject.decs.spa.fl_str_mv	Epistasis Genética Personas con Discapacidades Mentales
dc.subject.decs.eng.fl_str_mv	Epistasis, Genetic Persons with Mental Disabilities
dc.subject.proposal.spa.fl_str_mv	Epistasis Aprendizaje de máquinas Polimorfismo de un solo nucleótido Trastornos del neurodesarrollo Discapacidad intelectual
dc.subject.proposal.eng.fl_str_mv	Epistasis Machine learning Single nucleotide polymorphism Neurodevelopmental disorders Intellectual disability
dc.subject.wikidata.none.fl_str_mv	aprendizaje automático machine learning
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-05-16T19:38:59Z
dc.date.available.none.fl_str_mv	2024-05-16T19:38:59Z
dc.date.issued.none.fl_str_mv	2024-04-29
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/86100
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/86100 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	Bireme
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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institution	Universidad Nacional de Colombia
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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_abf2Niño Vásquez, Luis Fernandoefd9c4dcbf3d130ac1cd2d85e3072b2f600López Rivera, Juan Javier3d5c5646297ac533d82f13d1bca8415f600Murcia Triviño, Jossie Esteban915340edbb9886409664b5c8ec030c3a600laboratorio de Investigación en Sistemas Inteligentes Lisi2024-05-16T19:38:59Z2024-05-16T19:38:59Z2024-04-29https://repositorio.unal.edu.co/handle/unal/86100Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLos estudios de asociación como epistasis representan un factor importante en la comprensión de la expresión de enfermedades complejas, como lo son los trastornos del neurodesarrollo (TND), que presentan un desafío en el entendimiento de su etiología. Aunque varios estudios han revelado diferentes hallazgos de mutaciones, los efectos de asociación entre polimorfismos de un solo nucleótido (SNP) siguen siendo desconocidos. La reducción de dimensionalidad multifactorial (MDR) es un método de minería de datos por inducción constructiva empleado para detectar interacciones complejas. Este estudio comprendió una cohorte retrospectiva de pacientes pediátricos con prueba de exoma trio por sospecha de alteraciones genéticas para TND. Después de los controles de calidad sobre genotipos, se desarrolló el método MDR bajo la Prueba de desequilibrio de pedigrí (MDR-PDT). Además, se identificaron variantes asociadas individualmente con la enfermad a partir de la prueba de desequilibrio de transmisión (TDT). Se encontró que la variante rs6843524 (SEC24D) significativa por TDT (valor-P=0.003135) evidenció asociaciones con SNP; rs6843524-rs895952 (MDR-PDT valor-P=0.0084) y rs6843524-rs1168666 (MDR-PDT valor-P=0.0079). Aunque las variantes rs1168666 (SETD1B) y rs4974081 (QRICH1) no fueron significativas en MDR, si se identificaron en varios modelos y sus genes destacaron en el análisis de enriquecimiento (FDR 1.11e-05 y 6.55e-05). A pesar de la baja significancia de los modelos MDR-PDT, se lograron validar asociaciones importantes por medio de las otras pruebas y la interpretación biológica. Estos modelos pueden ser muy útiles en el descubrimiento de nuevas variantes, especialmente cuando son desarrollados sobre poblaciones grandes y con un análisis completo desde la secuenciación. (Texto tomado de la fuente).Association studies such as epistasis studies represent an important factor in understanding the expression of complex diseases, such as neurodevelopmental disorders (NDD). These disorders exhibit a challenge around their etiology. Even though certain studies have revealed several mutation findings, the association effects between Single Nucleotide Polymorphisms (SNPs) remain unknown. Multifactor dimensionality reduction (MDR) is a constructive induction data mining approach that can be used to identify those effects. In this work, a retrospective cohort study based on pediatric patients with trio exome analysis due to suspected genetic alterations for NDD was carried out. After developing genotype quality controls, MDR method was performed under Pedigree Imbalance Test (MDR-PDT). In addition, variants individually associated to disease were identified with Transmission Disequilibrium Test (TDT). We found that variant rs6843524 (SEC24D) is TDT significant (P-value=0.003135) and evidenced SNP interactions; rs6843524-rs895952 (MDR-PDT P-value=0.0084) and rs6843524-rs1168666 (MDR-PDT P-value=0.0079). Although variants rs1168666 (SETD1B) and rs4974081 (QRICH1) were not significant by MDR they were identified by several models and their genes were outstanding in enrichment analysis (FDR 1.11e-05 y 6.55e-05). Despite the low significance of MDR-PDT models, important associations were validated through other tests and biological interpretation. These models can be very useful in discovering new variants, especially when they are developed on larger populations and performing a complete analysis beginning from sequencing.MaestríaMagíster en BioinformáticaBioinformática funcional y estructuralxv, 103 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en BioinformáticaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores610 - Medicina y salud::616 - EnfermedadesEpistasis GenéticaPersonas con Discapacidades MentalesEpistasis, GeneticPersons with Mental DisabilitiesEpistasisAprendizaje de máquinasPolimorfismo de un solo nucleótidoTrastornos del neurodesarrolloDiscapacidad intelectualEpistasisMachine learningSingle nucleotide polymorphismNeurodevelopmental disordersIntellectual disabilityaprendizaje automáticomachine learningModelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrolloMachine learning-based epistasis model for intellectual disability and neurodevelopmental delayTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBiremeAbay-Nørgaard, S., Attianese, B., Boreggio, L., & Salcini, A. 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Modelo de epistasis basado en aprendizaje automático para pacientes con discapacidad intelectual y retraso del neurodesarrollo

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