The increasingly commonplace generation of genome-scale data provides us with a wealth of biological knowledge that captures global molecular-level changes in diverse model organisms and humans. However, these large data are often noisy, highly heterogenous, and lack the resolution required to study key aspects of metazoan complexity, such as tissue and cell-type specificity. In this primer, we will discuss a semi-supervised Bayesian network integration approach that leverages such large data compendia in concert with biological knowledge derived from small scale experiments to predict functional relationships between genes. We will then explore some of the applications of these models of tissue and cell function, including the prioritization of novel disease candidate genes based on genome-wide association studies (GWAS). Finally, we will demo publicly available web servers that provide interfaces to many of the analyses described here.