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

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

The facility of next‐generation sequencing has led to an explosion of gene catalogs for
novel genomes, transcriptomes and metagenomes, which are functionally uncharacterized …

Motivation Automated function prediction (AFP) of proteins is a large-scale multi-label
classification problem. Two limitations of most network-based methods for AFP are (i) a …

Motivation Facing the increasing gap between high-throughput sequence data and limited
functional insights, computational protein function annotation provides a high-throughput …

Protein function prediction is critical for understanding the cellular physiological and
biochemical processes, and it opens up new possibilities for advancements in fields such as …

The structure of a protein is of great importance in determining its functionality, and this
characteristic can be leveraged to train data-driven prediction models. However, the limited …

Drug combinations have exhibited promising therapeutic effects in treating cancer patients
with less toxicity and adverse side effects. However, it is infeasible to experimentally screen …

Motivation Protein function annotation is fundamental to understanding biological
mechanisms. The abundant genome-scale protein–protein interaction (PPI) networks …

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery.
Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of …