Graph Representation Learning in Biomedicine

TL;DR

This review discusses how graph representation learning, grounded in systems biology principles, has advanced biomedical applications like disease analysis, drug discovery, and personalized medicine by embedding complex biological networks into vector spaces.

Contribution

It connects systems biology principles with graph learning techniques, explaining their success, limitations, and future potential in biomedical research.

Findings

Graph embedding improves disease variant identification

Enhances understanding of single-cell behaviors

Aids in diagnosis and drug development

Abstract

Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge. Advances in artificial intelligence, specifically deep learning, have enabled us to model, analyze, and learn with such networked data. In this review, we put forward an observation that long-standing principles of systems biology and medicine -- while often unspoken in machine learning research -- provide the conceptual grounding for representation learning on graphs, explain its current successes and limitations, and even inform future advancements. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces. We also capture the breadth of ways in which representation learning has dramatically improved the state-of-the-art in biomedical machine learning. Exemplary domains covered include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines.