Molecular indicators for health, disease and response to treatment are increasingly guiding personalized healthcare. At the same time, single biomarkers will never fully reflect the interplay between the genetic make-up and environmental exposure. This asks for a more holistic view on human health, and integrated analyses of large-scale molecular and clinical data.
This course will lay a firm basis for such an integrative approach. It provides a comprehensive overview of state-of-the-art biomarker discovery strategies using -omics analysis platforms, gives practical cues to the most fit-for-purpose experimental approaches, based on real-life examples.
The basic principles and workflows of genomics, proteomics, metabolomics and data analysis, integration and stewardship will be covered.
The course will contain demonstrations and hands-on computer practical's of commonly used open-source analysis and visualization packages. Special attention will be paid to the different analysis to integrate X-omics data from different levels, ranging from pure statistical integration approaches (including machine learning) to knowledge-based data integration.
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