Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis
Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of t...
| Autores: | , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/194949 |
| Acceso en línea: | https://hdl.handle.net/2445/194949 |
| Access Level: | acceso abierto |
| Palabra clave: | Transcripció genètica Microbiota intestinal Glioma Hematopoesi Genetic transcription Gastrointestinal microbiome Gliomas Hematopoiesis |
| Sumario: | Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn's disease. |
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