The major challenge in analysing omic datasets is the strong dependencies which are present between samples and features. Taking into account and modelling the different dependency structures can... Show moreThe major challenge in analysing omic datasets is the strong dependencies which are present between samples and features. Taking into account and modelling the different dependency structures can lead to further improvements of our knowledge of the biological mechanisms. Therefore, improving our ability to predict diseases. This dissertation focuses on the development of new statistical methods designed to take into account the existing structures inside omic datasets by using mixed models, Gaussian graphical models, and machine learning approaches. Show less
The scope of this thesis spanned several issues in the measurement and evaluation of OD. The screening, assessment, and treatment effect for OD have been covered,with a special emphasis on... Show moreThe scope of this thesis spanned several issues in the measurement and evaluation of OD. The screening, assessment, and treatment effect for OD have been covered,with a special emphasis on patient self-evaluation. Show less
SDHD-related head and neck paragangliomas are, hereditary and generally benign, neuroendocrine tumors that arise from paraganglionic tissue associated with the parasympathetic nervous system.... Show moreSDHD-related head and neck paragangliomas are, hereditary and generally benign, neuroendocrine tumors that arise from paraganglionic tissue associated with the parasympathetic nervous system. The primary aim of this thesis was to gain more insight in the natural course of SDHD-related head and neck paragangliomas and ultimately improve surveillance and treatment strategies, as well as counseling of both patients and their family members. The risk of occult and metachronous paragangliomas, tumor growth, clinical progression and survival of SDHD germline mutation carriers were addressed. Show less
The work presented in this thesis focuses on methods for the construction of diagnostic rules based on clinical mass spectrometry proteomic data. Mass spectrometry has become one of the key... Show moreThe work presented in this thesis focuses on methods for the construction of diagnostic rules based on clinical mass spectrometry proteomic data. Mass spectrometry has become one of the key technologies for jointly measuring the expression of thousands of proteins in biological samples. However, the development of MS instrumentation gave rise to new statistical challenges in the processing and analysis of the acquired data. This is due to the complex nature of the spectral proteomic signal which is measured, as it consists of high-dimensional functions representing the within-patient proteome expression. This work considers new methods to respond to these challenges. Our main interest focuses on the comparison of mass spectral proteomic profiles collected from healthy individuals and cancer patients in the context of distinct case-control studies. A key objective in such studies is the construction of discriminant rules for distinguishing between individuals as to the presence or absence of the disease as well as for predicting the health status of future patients. We present a series of data analyses for distinct case-control cancer studies where we address these questions through the use of methodology specific for the type of data considered in each of the studies. Show less
