Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …

The prevalence of graph structures in real-world scenarios enables important tasks such as
node classification and link prediction. Graphs in many domains follow a long-tailed …

Motivation Graphs or networks are widely utilized to model the interactions between different
entities (eg proteins, drugs, etc.) for biomedical applications. Predicting potential …

Everything is connected and thus networks are instrumental in not only modeling complex
systems with many components, but also accommodating knowledge about their …

Motivation Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer
drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off …

Disease–gene association through genome-wide association study (GWAS) is an arduous
task for researchers. Investigating single nucleotide polymorphisms that correlate with …

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability,
while mutation of either gene does not affect the cell survival. The potential usage of …

Motivation Human microbes get closely involved in an extensive variety of complex human
diseases and become new drug targets. In silico methods for identifying potential microbe …

Motivation human microbes play a critical role in an extensive range of complex human
diseases and become a new target in precision medicine. In silico methods of identifying …