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Data-driven insights from immunogenomics, metabolomics, and clinical mental health data


In recent years, we have seen the rise of technologies with unprecedented abilities to query the status of different biological systems that generate enormous amounts of data. While these data hold the promise to unravel new insights and better understanding of the working of the human biological systems, this access to enormous amounts of data is frequently accompanied by computational and algorithmic challenges. This dissertation answers questions related to some of these systems with the help of data – in some cases by developing new algorithms and computational tools, and in others using statistical and exploratory data analyses approaches.

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