Large-scale genomics data hold the promise of characterizing the molecular mechanisms underlying disease development and progression. Not only has their availability revolutionized basic research, but it has also started to affect clinical practice, paving the way for personalized medicine. A major challenge in genomics research is the development of computational methods that are able to exploit experimental data in order to understand the complex relationships between the components of cellular systems. During my talk I will present a Bayesian clustering approach for temporal expression data and a strategy based on dynamic Bayesian networks for the inference of cellular networks discussing their application to relevant problems in functional genomics.
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