Rapid technological advances have led to the production of different types of biological data
and enabled construction of complex networks with various types of interactions between …

Background Machine learning (ML) is increasingly used in research for subtype definition
and risk prediction, particularly in cardiovascular diseases. No existing ML models are …

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification
of drug-target interactions. However, only a small portion of the drug-target interactions have …

Less than half of patients with suspected genetic disease receive a molecular diagnosis. We
have therefore integrated next-generation sequencing (NGS), bioinformatics, and clinical …

Cancer genomics has produced extensive information on cancer-associated genes, but the
number and specificity of cancer-driver mutations remains a matter of debate. We …

Network-based analytics plays an increasingly important role in precision oncology.
Growing evidence in recent studies suggests that cancer can be better understood through …

Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The
onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons …

Network embedding aims to learn a low-dimensional vector representation for each node in
the social and information networks, with the constraint to preserve network structures. Most …

Background Cancer subtype information is critically important for understanding tumor
heterogeneity. Existing methods to identify cancer subtypes have primarily focused on …

Paralleling the increasing availability of protein-protein interaction (PPI) network data,
several network alignment methods have been proposed. Network alignments have been …