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Integration of omics and non-omics data in pancreatic cancer research

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dc.contributor Universitat de Vic - Universitat Central de Catalunya. Facultat de Ciències i Tecnologia
dc.contributor Universitat de Vic - Universitat Central de Catalunya. Màster Universitari en Anàlisi de Dades Òmiques
dc.contributor.author Alarcón Moreno, Pablo
dc.date.accessioned 2019-04-03T16:51:04Z
dc.date.available 2019-04-03T16:51:04Z
dc.date.created 2018-09-17
dc.date.issued 2018-09-17
dc.identifier.uri http://hdl.handle.net/10854/5759
dc.description Curs 2017-2018 es
dc.description.abstract Background: With the wide-spreading in high-throughput technologies and the arrival of omics era, new bioinformatics tools are available for the integration of different data types. Epidemiological and translational integrative research needs to combine newly generated omics data with classical risk and prognostic/predictive factors to increase the accuracy of their models/algorithms. This need adds an additional layer of complexity in the field of integromics that has barely been addressed. Here, I assessed the performance of some available integrative methods, such as LASSO, Elastic Net (ENET), Integrative clustering by iClusterPlus, and Neural Networks for the integration of omics and non-omics variables in the same model. Methods: An in-depth bibliographic search was conducted to identify, learn about, characterize, and select data integrative methods. Then, using the data generated by the PanGenEU Study on pancreatic cancer, I created two different datasets composed both by omics (genomic and epigenomics) variables and epidemiological variables. Finally, I have applied the integrative methods to both datasets. Results: ENET and IClusterPlus had reported both omics and non-omics as feature selection, being diabetes the only epidemiological variable selected for both methods. LASSO and ENET have selected common omics variables that overlaps with previous studies performed in pancreatic cancer research. Neural Network could not be used with both datasets but it was applied to ENET’s feature selection, obtaining a high level of accuracy. Conclusions: All the integrative methods have presented several advantages and difficulties in both omics and non-omics integration. I propose Elastic Net as the best method applied, due to feature selection reported and low run time and computational requirements. es
dc.format application/pdf es
dc.format.extent 51 p. es
dc.language.iso eng es
dc.rights Tots els drets reservats es
dc.subject.other Bioinformàtica es
dc.subject.other Biologia computacional es
dc.subject.other Models biològics es
dc.subject.other Pàncrees -- Càncer -- Investigació es
dc.title Integration of omics and non-omics data in pancreatic cancer research es
dc.type info:eu-repo/semantics/masterThesis es
dc.rights.accesRights info:eu-repo/semantics/closedAccess es

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