Graph representation learning in biomedicine and healthcare

Networks-or graphs-are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines. This Perspective outlines the successes and limitations of graph deep learning for biomedical and healthcare applications.

Automated summary

This Perspective discusses the use of representation learning, particularly graph representation learning, in biomedical and healthcare applications. It argues that graph representation learning will continue to advance machine learning in the fields of medicine and healthcare, and outlines potential applications and directions for development.