The chromosomes of the human genome are organized in three-dimensions by compartmentalizing the cell nucleus and different genomic loci also interact with each other. However, the principles underlying such nuclear genome organization and its functional impact remain poorly understood. In this talk, I will introduce some of our recent work in developing machine learning methods by utilizing whole-genome mapping data to study the higher-order genome organization. Our methods reveal the spatial localization of chromosome regions and exploit chromatin interactome patterns within the cell nucleus in different cellular conditions, across mammalian species, and also in single-cell resolution. We hope that these algorithms will provide new insights into the principles of nuclear spatial organization.