Graph-Based Gene Embedding.

Gene embeddings, concise numerical descriptions of complex genetic relationships, are powerful tools for predicting unknown functions of genes and drugs. The application of graph-specific representation learning algorithms on large biological network databases allows for the efficient computation of highly expressive gene embeddings. This chapter provides an explanation of the end-to-end calculation of such embeddings, with a focus on the most widely used and generally applicable datasets and algorithms.