| Autor: |
Hidalgo, Marta R, Català-Senent, José F, Pérez-Díez, Irene, Merlo, Pablo M Malmierca, Ferrer, Franc Casanova, Moraga, Raúl Pérez, Risco, Rubén Grillo, Lopez-Cerdan, Adolfo, García, Ruben Sánchez, Berenguer, Marina, Pascual, María, Guerri, Consuelo, Barquín, Miguel, Atocha Romero, Farràs, Rosa, Iglesia-Vayá, María De La, Galiana, Laura, Tomas, Jose M, Oliver, Amparo, Burks, Deborah, García-García, Francisco |
| Rok vydání: |
2018 |
| DOI: |
10.6084/m9.figshare.7326503 |
| Popis: |
Clinical and epidemiological indicators show a large number of diseases and health scenarios where gender differences are detected but the underlying causes of these changes are still unknown. The detection of these biological, clinical and psychosocial causes would allow us to be more precise in the diagnosis and in the personalized selection of treatments for each group of patients, and to improve the selection of health interventions. In this study, we present several big data approaches whose goal is to improve the knowledge that explain gender differences in 1) non small cell lung cancer, 2) non alcoholic fatty liver disease, 3) effect of alcohol on development, 4) schizophrenia and 5) loneliness. For the three first scenarios, the strategy includes three phases: i) the systematic review and selection of omics studies available in public repositories such as Gene Expression Omnibus, Sequence Read Archive, The Cancer Genome Atlas... ii) the analysis of the data of each study in both signalling pathways and molecular functions contexts, highlighting those with clear alterations in their activity. iii) finally, the application of a functional meta-analysis to all results to provide a better interpretation in a Systems Biology approach. For the schizophrenia study, we explain gender differences evaluating biomedical images and for loneliness we do so through systematic review of recent literature. From these big data approaches we provide bottom-up prototypes to generalize at a population level in a next step. These results allow us to know the common functions in the set of the omics studies for one or more diseases, offering a greater power in the detection of signaling routes or functions of interest, which will provide us with new and more effective therapeutic targets within the framework of Precision Medicine. This knowledge would provide evidence based useful information in prevention and health stakeholders’ decision-making. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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