Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Recent advancements in experimental high-throughput technologies have expanded the
availability and quantity of molecular data in biology. Given the importance of interactions in …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

Graph neural networks have-enabled the application of deep learning to problems that can
be described by graphs, which are found throughout the different fields of sci-ence, from …

Medication recommendation is an important healthcare application. It is commonly
formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal …

Combinatorial drug recommendation involves recommending a personalized combination of
medication (drugs) to a patient over his/her longitudinal history, which essentially aims at …

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic
in both academia and industry. A recent study showed that using the graphical structure …

Graphs are useful data structures in complex real-life applications such as modeling
physical systems, learning molecular fingerprints, controlling traffic networks, and …

Medication recommendation is an essential task of AI for healthcare. Existing works focused
on recommending drug combinations for patients with complex health conditions solely …

Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving
offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However …