PROJECT SUMMARY We propose to develop and refine neural networks gene expression to understand gene regulation in diseases. We will design deep learning frameworks to integrate various datasets (histone modifications, 3D conformation, sequences, and SNPs) and model their relationship with the gene expression. Our proposed models will explicitly capture the underlying structure and complexity of the biological data to learn meaningful connections. For example, we will use a graph-based neural network to model the 3D conformation of the DNA as a graph and learn from the connections between different genomic regions to predict gene expression. One of our critical goals for using these methods is to extract relevant signals that could be contributing to the up- and down-regulation of genes. We will accomplish this goal by applying interpretation methods for neural networks. These methods will allow us to assign importance scores to the input features that contribute the most towards a particular prediction of interest. Comparing these scores for genes across healthy and disease cell lines will provide insights into gene misregulation and serve as a hypothesis driving tool for biological experiments. We also propose a novel Bayesian inference-based interpretation method to improve explanations of graph-based neural networks that could be applied to various tasks. Finally, given the improvement of single-cell technologies and imputation methods, we will extend our deep learning frameworks to model relationships between signals like chromatin accessibility and DNA methylation with gene expression. This direction will allow us to explore the effectiveness of the imputation methods in removing noise and generating high-quality single-cell samples for usage in deep learning modeling of gene regulation. Looking at the modeled relationships across the cell's developmental stages could pinpoint timepoints for potential misregulation in diseases. Therefore, this proposal aims to develop unified approaches that utilize datasets spanning multiple repositories to leverage their collective knowledge and improve our understanding of diseases in a data-driven manner.