Multiomics Data Integration for Gene Regulatory Network Inference with Exponential Family Embeddings

The advent of omics technologies have enabled the generation of huge, complex, heterogeneous, and high-dimensional omics data. Imposing numerous challenges in data integration, these data could lead to a better understanding of the organism's cellular system. Omics data are typically represented as networks to study relations between biological entities, such as protein-protein interaction, gene regulation, and signal transduction. To this end, network embedding approaches allow us to learn latent feature representations for nodes of a graph structure. In this study, we propose a new methodology to learn embeddings by modeling the underlying interactions among biological entities (nodes) with exponential family distributions from a well chosen set of omics modalities. We evaluate our proposed method based on the gene regulatory network (GRN) inference problem. As the ground truth for evaluation, we use GRN available in public databases and demonstrate its effectiveness by comparing to other network integration approaches.