In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, …

The identification of gaps, open problems, and promising future research directions in ML applied to GSP in healthcare. The remainder of the paper is organized as follows. In Section 2, the …

Commonly used graph ML approaches for these two levels are shallow embedding and graph neural networks (GNN). This study performs comprehensive research to identify articles that …

Jun 2, 2020 · Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this …

Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining.

Graph machine learning approaches, also known as geometric deep learning, or graph neural networks has become widely used in biomedical applications.

Biological systems are naturally represented as networks! Drugs: DrugBank, PubChem, ChEBI... Disease: MeSH, DiseaseOntology, DDB,... Adverse events: MedDRA, ADReCS,... How can …

Jun 1, 2022 · Five major medical applications are deeply discussed, focusing on adapting the ML models to solve the problems in cancer, medical chemistry, brain, medical imaging, and …

2 days ago · Here, we propose Graph-Encoded Mixture Survival (GEMS) as a general machine learning framework to identify distinct predictive subphenotypes that guarantee coherent …

Oct 31, 2022 · In this Perspective, we survey the capabilities of graph representation learning and highlight notable applications in biomedicine and healthcare.