The task of predicting the interactions between drugs and targets plays a key role in the
process of drug discovery. There is a need to develop novel and efficient prediction …

Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …

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

Abstract GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular
metabolism and physiological states in living organisms. However, due to our imperfect …

Accurate prediction of drug-target interactions (DTIs) through biological data can reduce the
time and economic cost of drug development. The prediction method of DTIs based on a …

Link prediction insimple graphs is a fundamental problem in which new links between
vertices are predicted based on the observed structure of the graph. However, in many real …

As a natural extension of link prediction on graphs, hyperlink prediction aims for the
inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than …

Abstract Motivation Predicting Drug-Drug Interaction (DDI) has become a crucial step in the
drug discovery and development process, owing to the rise in the number of drugs co …

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading
cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified …

Synthetic lethality (SL) is a very important concept for the development of targeted
anticancer drugs. However, experimental methods for SL detection often suffer from various …