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Title: | Integración de variables clínicas y de expresión génica en un modelo estadístico para la valoración pronóstica en pacientes con cáncer de mama |
Author: | Nieto Moragas, Javier |
Tutor: | Gonzalo Sanz, Ricardo |
Others: | Universitat Oberta de Catalunya Morán Moreno, Jose Antonio Ventura, Carles |
Abstract: | Despite early detection programs, the use of higher resolution imaging techniques or the greater specificity of chemotherapy treatments, cancer remains one of the leading causes of mortality in the population. For the breast cancer, despite the early diagnosis, the improvement in the treatment and the increase in the cure rate, the survival rate after 10 years of remission is around 80% in western coutries. Several authors have demonstrated the added value by including the measurement of genetic expression or the identification of patterns in the improvement of the diagnosis or the treatment. Other authors have described the utility of integrating variables of gene expression and variables obtained during clinical practice in a statistical model without an overfitting. After the analysis and the selection of clinical and gene expression variables from a public database, a LASSO classifier with good diagnostic performance was obtained. The application of the model in an independent cohort shows an acceptable performance. However, only a small improvement is observed when clinical variables are included in the models with the gene expression variables. |
Keywords: | breast cancer microarrays pipeline |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jan-2018 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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Presentación TFM_Javier_Nieto_Moragas.ppsx | Presentación Power Point | 18,82 MB | Unknown | View/Open |
GSE21653_series_matrix.txt.gz | Datos del estudio | 21,98 MB | Unknown | View/Open |
xnietomoragasTFM0118memoria.pdf | Memoria del TFM | 3,23 MB | Adobe PDF | View/Open |
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