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The genome contains highly detailed regulatory instructions that modulate dynamic programs of gene expression across cell types and tissues. We develop computational methods to characterize these regulatory instructions, understand how the cell interprets them, and create models that predict how defects in these instructions can affect the cell function and, eventually, human health. Our research encompasses the study of transcription factors, RNA-binding proteins, and non-coding RNAs. We combine machine learning and statistical inference with large-scale genomic datasets to characterize gene regulatory networks of these factors and their association with complex human diseases.