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 …

Drug–drug interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …

Background: Determining drug–disease associations is an integral part in the process of
drug development. However, the identification of drug–disease associations through wet …

Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …

Identifying new indications for drugs plays an essential role at many phases of drug
research and development. Computational methods are regarded as an effective way to …

Drug repositioning is an efficient and promising strategy for traditional drug discovery and
development. Many research efforts are focused on utilizing deep-learning approaches …

In silico reuse of old drugs (also known as drug repositioning) to treat common and rare
diseases is increasingly becoming an attractive proposition because it involves the use of de …

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …

Motivation There are various interaction/association bipartite networks in biomolecular
systems. Identifying unobserved links in biomedical bipartite networks helps to understand …

Molecular interaction networks are powerful resources for molecular discovery. They are
increasingly used with machine learning methods to predict biologically meaningful …