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Evaluation of hierarchical models for integrative genomic analyses

Motivation: Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data. Results: Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients' survival. (Résumé d'auteur)

Mots-clés : Évaluation; méthodologie; analyse de données; biologie moléculaire; phénotype; marqueur génétique; Étude de cas; génomique; adénome; néoplasme; maladie de l'appareil génital fém; cerveau; maladie de l'homme; méthode statistique; génie génétique; bioinformatique; modèle mathématique; modèle de simulation; algorithme

Thématique : Méthodes mathématiques et statistiques; Autres thèmes; Maladies des animaux; Méthodes de recherche

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