Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama
ilustraciones, diagramas
- Autores:
-
Eyrolle-Cellier, Samuel
- 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/85731
- Palabra clave:
- 610 - Medicina y salud::616 - Enfermedades
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Biología Computacional
Aprendizaje Automático
Neoplasias
Computational Biology
Machine Learning
Neoplasms
Cáncer de mama
Cáncer de endometrio
Integración Multi-ómica
Aprendizaje multi-vista
Biomarcadores
Breast Cancer
Endometrial Cancer
Multi-Omics Integration
Multi-view Learning
Clustering
Biomarkers
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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UNACIONAL2 |
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Universidad Nacional de Colombia |
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|
| dc.title.spa.fl_str_mv |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| dc.title.translated.eng.fl_str_mv |
Machine learning model for integrating genomic, epigenomic, transcriptomic and clinical data from endometrial cancer and breast cancer studies |
| title |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| spellingShingle |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama 610 - Medicina y salud::616 - Enfermedades 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Biología Computacional Aprendizaje Automático Neoplasias Computational Biology Machine Learning Neoplasms Cáncer de mama Cáncer de endometrio Integración Multi-ómica Aprendizaje multi-vista Biomarcadores Breast Cancer Endometrial Cancer Multi-Omics Integration Multi-view Learning Clustering Biomarkers |
| title_short |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| title_full |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| title_fullStr |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| title_full_unstemmed |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| title_sort |
Modelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mama |
| dc.creator.fl_str_mv |
Eyrolle-Cellier, Samuel |
| dc.contributor.advisor.spa.fl_str_mv |
Niño Vásquez, Luis Fernando |
| dc.contributor.author.spa.fl_str_mv |
Eyrolle-Cellier, Samuel |
| dc.contributor.researchgroup.spa.fl_str_mv |
laboratorio de Investigación en Sistemas Inteligentes Lisi |
| dc.subject.ddc.spa.fl_str_mv |
610 - Medicina y salud::616 - Enfermedades 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
610 - Medicina y salud::616 - Enfermedades 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Biología Computacional Aprendizaje Automático Neoplasias Computational Biology Machine Learning Neoplasms Cáncer de mama Cáncer de endometrio Integración Multi-ómica Aprendizaje multi-vista Biomarcadores Breast Cancer Endometrial Cancer Multi-Omics Integration Multi-view Learning Clustering Biomarkers |
| dc.subject.decs.spa.fl_str_mv |
Biología Computacional Aprendizaje Automático Neoplasias |
| dc.subject.decs.eng.fl_str_mv |
Computational Biology Machine Learning Neoplasms |
| dc.subject.proposal.spa.fl_str_mv |
Cáncer de mama Cáncer de endometrio Integración Multi-ómica Aprendizaje multi-vista Biomarcadores |
| dc.subject.proposal.eng.fl_str_mv |
Breast Cancer Endometrial Cancer Multi-Omics Integration Multi-view Learning Clustering Biomarkers |
| description |
ilustraciones, diagramas |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-11-03 |
| dc.date.accessioned.none.fl_str_mv |
2024-02-27T19:13:28Z |
| dc.date.available.none.fl_str_mv |
2024-02-27T19:13:28Z |
| 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/85731 |
| 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/85731 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 |
| dc.relation.references.spa.fl_str_mv |
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International Journal of Molecular Medicine, 26(1). https://doi.org/10.3892/ijmm_00000435 Yaghmaie, F., Saeed, O., Garan, S. A., Freitag, W., Timiras, P. S., & Sternberg, H. (2005). Caloric restriction reduces cell loss and maintains estrogen receptor-alpha immunoreactivity in the pre-optic hypothalamus of female B6D2F1 mice. Neuro Endocrinology Letters, 26(3), 197–203. |
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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 Fernandobc784b82735e16fe53653c3f5c8f3bbeEyrolle-Cellier, Samuel9205581c865b7dda83578c42cdf1680alaboratorio de Investigación en Sistemas Inteligentes Lisi2024-02-27T19:13:28Z2024-02-27T19:13:28Z2023-11-03https://repositorio.unal.edu.co/handle/unal/85731Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl cáncer de mama y el cáncer de endometrio son enfermedades complejas que presentan mucha heterogeneidad a nivel molecular e histológico. Ciertos pacientes de estos dos tipos de cáncer comparten tanto mecanismos moleculares y celulares, como factores causales, como lo es el hiperestrogenismo. Este proyecto de investigación buscó identificar biomarcadores tumorales compartidos entre ambas enfermedades. 565 pacientes con cáncer de mama y 348 con cáncer de endometrio de la plataforma The Cancer Genome Atlas fueron seleccionados según sus características histológicas, hormonales e inmunológicas. Sus datos ómicos fueron analizados de manera separada e integrada mediante el uso del algoritmo de aprendizaje multi-vista Deep Generalized Canonical Correlation Analysis y del método de reducción de dimensionalidad Uniform Manifold Approximation and Projection. Se extrajeron biomarcadores de cada grupo (cluster) a través del cálculo del puntaje de información mutua entre las variables iniciales y las variables sintéticas UMAP1 y UMAP2. El análisis de los biomarcadores reveló que varios de estos genes tienen un rol en la proliferación celular, la apoptosis y la angiogénesis. Así mismo, el análisis reveló que la ausencia de metilación en las regiones promotoras de CLTC, importante en la organización del huso mitótico, y SON, involucrado en el empalme del ARN, es una característica compartida entre muchos pacientes de la cohorte. Por otro lado, FBXO11 y PTPN11 se caracterizan por niveles altos de expresión génica en ambos tipos de cáncer. FBXO11 codifica para una ubiquitina ligasa necesaria para la degradación proteica; mientras que PTPN11 codifica para una tirosina fosfatasa que actúa en la transducción de señales mediante una regulación positiva de la vía de señalización RAS/RAF/MAPK. En conclusión, la estrategia de integración multi-ómica permitió descubrir biomarcadores que no aparecen en el análisis de datos ómicos de un solo tipo. Se inscribe como una prueba de concepto de integración de distintos tipos de datos provenientes de diferentes contextos patológicos en el campo de la oncología. (Texto tomado de la fuente).Breast cancer and endometrial cancer are complex diseases that show a high degree of molecular and histological heterogeneity. Certain patients with these two types of cancer share both molecular and cellular mechanisms, as well as causal factors such as hyperestrogenism. This research project aimed to identify shared tumor biomarkers between both diseases. 565 breast cancer patients and 348 endometrial cancer patients from The Cancer Genome Atlas platform were selected based on their histological, hormonal, and immunological characteristics. Their omics data was analyzed separately and integratively using the multi-view learning algorithm Deep Generalized Canonical Correlation Analysis and the dimensionality reduction method Uniform Manifold Approximation and Projection. Biomarkers were extracted from each cluster by calculating the mutual information score between the initial variables and the UMAP1 and UMAP2 synthetic variables. The analysis of the biomarkers revealed that several of these genes play a role in cell proliferation, apoptosis, and angiogenesis. Additionally, the analysis showed that the absence of methylation in the promoter regions of CLTC, which is important in the organization of the mitotic spindle, and SON, involved in RNA splicing, is a shared characteristic among many patients in the cohort. On the other hand, FBXO11 and PTPN11 are characterized by high levels of gene expression in both types of cancer. FBXO11 encodes for a ubiquitin ligase necessary for protein degradation, while PTPN11 encodes for a tyrosine phosphatase that acts in signal transduction by positively regulating the RAS/RAF/MAPK signaling pathway. In conclusion, the multi-omic integration strategy allowed the discovery of biomarkers that have not been identified in the omics data analysis of a single type. It serves as a proof of concept for integrating different types of data from different pathological contexts in the field of oncology.MaestríaMagíster en BioinformáticaLa primera fase del proyecto corresponde a la selección de los datos y está asociada al primer objetivo específico de selección de los conjuntos de datos genómicos, epigenómicos, transcriptómicos y clínicos de pacientes con cáncer de endometrio y con cáncer de mama susceptibles de compartir tanto mecanismos moleculares, celulares e inmunológicos como factores causales en la plataforma TCGA. Esta fase tiene tres actividades: • Selección preliminar de los pacientes de interés mediante la explotación de los datos clínicos: se busca seleccionar los pacientes mujeres con cáncer ductal o lobulillar infiltrante positivos para los receptores de estrógenos y progesterona (cáncer de mama) y los pacientes con adenocarcinoma endometrioide (cáncer de endometrio). • Selección de los pacientes con un perfil hormonal de interés mediante la explotación de los datos transcriptómicos: primero, se determina la distribución del nivel de expresión de los receptores de estrógenos y progesterona en los pacientes; luego, se seleccionan los pacientes cuyo tumor tiene una expresión de ambos receptores de hormonas superior al umbral de positividad establecido. • Selección de los pacientes con una composición tumoral similar a nivel inmunológico mediante el uso de la herramienta Cibersort: se caracteriza el microentorno tumoral y el fenómeno de infiltración inmune en los pacientes. El entregable de esta fase es un listado de los pacientes seleccionados. La segunda fase del proyecto es el análisis exploratorio de los datos y busca cumplir con el segundo objetivo específico: determinar el tipo de modelo de aprendizaje automático más adecuado recurriendo a una exploración de los datos seleccionados. Esta fase también está divida en tres actividades: • Análisis de datos ómicos de un solo tipo mediante el uso de algoritmos existentes: se busca agrupar los pacientes seleccionados según su perfil genómico, epigenómico, o transcriptómico e identificar los patrones moleculares que rigen las agrupaciones obtenidas. • Análisis correlacional entre los distintos tipos de datos ómicos: se busca establecer una relación matemática entre el número de copias de los genes o la metilación del ADN y la expresión génica. • Análisis exploratorio de los datos clínicos: se identifican las variables clínicas completas para el conjunto de pacientes y se busca caracterizar los grupos de pacientes obtenidos durante el análisis de datos ómicos de un solo tipo por sus características clínicas. Los entregables de esta fase son: un reporte del análisis exploratorio para cada tipo de datos y tres listados de biomarcadores resultantes de los análisis de datos ómicos de un solo tipo. La tercera fase del proyecto es la integración de los datos. Es la fase crítica del proyecto y está asociada al tercer objetivo específico: desarrollar un modelo de aprendizaje automático de integración de los datos ómicos seleccionados. Esta fase contiene tres actividades: • Recolección y preparación de los datos: primero, se establece un consenso sobre el nombre de las características (genes, regiones metiladas y transcritos) para poder relacionarlas posteriormente; luego, se da el formato adecuado a los datos para la implementación del modelo de aprendizaje automático. • Desarrollo del modelo de aprendizaje automático: es un proceso cíclico, compuesto por tres etapas recurrentes, destinado a optimizar el modelo. La primera etapa es el entrenamiento del modelo. La segunda etapa corresponde a la prueba del modelo de aprendizaje automático, se evalúa el desempeño del modelo a través del cálculo de las métricas adecuadas. En la tercera etapa se lleva a cabo el ajuste del modelo, es decir, la optimización de los hiperparámetros del modelo (configuraciones utilizadas durante la etapa de entrenamiento). • Identificación de los biomarcadores compartidos mediante el uso del modelo optimizado y análisis clínico de los grupos de pacientes obtenidos en el proceso. Los entregables de la tercera fase son: un reporte de evaluación del modelo desarrollado y un listado de biomarcadores resultantes del uso del modelo. La cuarta fase de este estudio es la caracterización de los resultados obtenidos. Busca cumplir con el cuarto objetivo específico: caracterizar los biomarcadores compartidos identificados mediante el uso del método Gene Ontology. Esta fase admite dos actividades: • Caracterización biológica de los biomarcadores resultantes de los análisis de datos ómicos de un solo tipo y de los biomarcadores resultantes del uso del modelo de aprendizaje automático de integración de datos: se buscan los términos ontológicos (de la categoría proceso biológico) asociados con los biomarcadores identificados mediante el uso del método Gene Ontology. • Comparación de los biomarcadores identificados con las dos metodologías (análisis de datos ómicos de un solo tipo y uso del modelo): se profundiza el análisis de los biomarcadores encontrados tanto en el análisis de datos ómicos de un solo tipo como con el uso del modelo. El entregable de esta última fase es un reporte de la caracterización biológica de los biomarcadores identificados previamente (en la segunda y la tercera fase).Tecnologías computacionales en bioinformática145 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en BioinformáticaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - Enfermedades000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresBiología ComputacionalAprendizaje AutomáticoNeoplasiasComputational BiologyMachine LearningNeoplasmsCáncer de mamaCáncer de endometrioIntegración Multi-ómicaAprendizaje multi-vistaBiomarcadoresBreast CancerEndometrial CancerMulti-Omics IntegrationMulti-view LearningClusteringBiomarkersModelo de aprendizaje automático de integración de datos genómicos, epigenómicos, transcriptómicos y clínicos provenientes de estudios de cáncer de endometrio y de cáncer de mamaMachine learning model for integrating genomic, epigenomic, transcriptomic and clinical data from endometrial cancer and breast cancer studiesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBiremeAbeel, T., Helleputte, T., Van de Peer, Y., Dupont, P., & Saeys, Y. 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Neuro Endocrinology Letters, 26(3), 197–203.EstudiantesInvestigadoresMedios de comunicaciónPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85731/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL17EH20924.2023.pdf17EH20924.2023.pdfTesis de Maestría en Bioinformáticaapplication/pdf5674952https://repositorio.unal.edu.co/bitstream/unal/85731/2/17EH20924.2023.pdff22475f3e24602b07d9f9ebab2fd4aceMD52THUMBNAIL17EH20924.2023.pdf.jpg17EH20924.2023.pdf.jpgGenerated Thumbnailimage/jpeg5794https://repositorio.unal.edu.co/bitstream/unal/85731/3/17EH20924.2023.pdf.jpgf0cab28928ae1aec2f3eb5dcc26aded1MD53unal/85731oai:repositorio.unal.edu.co:unal/857312024-02-27 23:04:56.778Repositorio Institucional Universidad Nacional de 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