Interpretable Neural Networks in Genomics: Opening the Black Box

The overarching goal of functional genomics research is to understand and intervene in the biological processes that connect genotype, environment, and phenotype. Using data acquired from sequencing individuals, we can model these underlying mechanisms and aim to predict outcomes of interest, such as heritable traits or disease risk. This thesis explores the use of interpretable neural networks to predict complex traits and diseases from genomic data.