2024-03-28T20:09:22Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/6680532024-03-15T10:57:33Zcom_10803_236col_10803_690278
nam a 5i 4500
Omics
Statistical models
Data distribution
Bioinformatics
Ômiques
Models estadístics
Distribució de dades
Bioinformàtica
New approaches in omics data modelling
[Barcelona] :
Universitat Pompeu Fabra,
2019
Accés lliure
http://hdl.handle.net/10803/668053
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Nonell Mazelon, Lara,
autor
Programa de doctorat en Biomedicina,
degree
1 recurs en línia (175 pàgines)
Tesi
Doctorat
Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut
2019
Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut
Tesis i dissertacions electròniques
González Ruiz, Juan Ramón,
supervisor acadèmic
TDX
The breakthrough in the technological field has allowed the extraction of large
amounts of the so-called omics data. The analysis and Integration of this type of
data by means of advanced statistical and bioinformatics methods will allow the
improvement in the management of diseases. The diversity and complexity of
omics data has encouraged the development of hundreds of new statistical
methods to meet this objective. Therefore, having the appropriate methods to
accommodate different data distributions and modelling complex data structures
becomes essential. This thesis presents advances in three directions in this
regard. First, the study of several methods to assess non-linear associations
which is relevant when assessing the effect of environmental exposures (i.e
exposome) on complex diseases. The study is accompanied by the
development of the R package nlOmicAssoc. Second, the simplex distribution is
proposed to analyse methylome data since this distribution properly fits beta
values that are generated in this type of studies. The extension to generalized
linear models with simplex response is also proposed. Lastly, an R package,
HOmics, has been developed to incorporate a priori biological knowledge into
association studies by using Bayesian hierarchical models. It also implements
methods to model the dependence between omics data, enabling data
integration
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