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Machine learning in genomics and systems genetics

Our laboratory develops computational approaches to decipher molecular variations and their phenotypic consequences. Towards this goal, we develop approaches based on statistical inference and machine learning and apply them to molecular readouts and genetics systems. Together with experimental partners, we tackle biomedical questions, including principles of gene regulation, cell differentiation and molecular signatures in cancer.