  In biomedical applications of machine learning, relevant information often
has a rich structure that is not easily encoded as real-valued predictors.
Examples of such data include DNA or RNA sequences, gene sets or pathways, gene
interaction or coexpression networks, ontologies, and phylogenetic trees. We
highlight recent examples of machine learning models that use structure to
constrain model architecture or incorporate structured data into model
training. For machine learning in biomedicine, where sample size is limited and
model interpretability is critical, incorporating prior knowledge in the form
of structured data can be particularly useful. The area of research would
benefit from performant open source implementations and independent
benchmarking efforts.
