Poster Open Access

DeepGRN: Deciphering gene deregulation in cancer development using deep learning

Mathis, Roland; Manica, Matteo; Rodriguez Martinez, Maria

Understanding gene regulatory networks (GRNs) is key towards deciphering gene deregulation in cancer development. We are building on efforts to find tissue-specific and disease-specific gene regulatory networks. While large efforts have been devoted to create context specific GRNs for a range of tissues as well as diseases, most currently available cancer GRNs are inferred from unmatched datasets for which only the diseased tissue is available. Our goal is to find disease-specific changes of gene regulation using matched normal and tumor patient data in a cohort-specific fashion.

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