  Drug repositioning offers an effective solution to drug discovery, saving
both time and resources by finding new indications for existing drugs.
Typically, a drug takes effect via its protein targets in the cell. As a
result, it is necessary for drug development studies to conduct an
investigation into the interrelationships of drugs, protein targets, and
diseases. Although previous studies have made a strong case for the
effectiveness of integrative network-based methods for predicting these
interrelationships, little progress has been achieved in this regard within
drug repositioning research. Moreover, the interactions of new drugs and
targets (lacking any known targets and drugs, respectively) cannot be
accurately predicted by most established methods. In this paper, we propose a
novel semi-supervised heterogeneous label propagation algorithm named Heter-LP,
which applies both local as well as global network features for data
integration. To predict drug-target, disease-target, and drug-disease
associations, we use information about drugs, diseases, and targets as
collected from multiple sources at different levels. Our algorithm integrates
these various types of data into a heterogeneous network and implements a label
propagation algorithm to find new interactions. Statistical analyses of 10-fold
cross-validation results and experimental analysis support the effectiveness of
the proposed algorithm.
