Inferring Novel Indications of Approved Drugs via a Learning Method with Local and Global Consistency

Inferring new indication of approved drugs is critical not only for the elucidation of the interaction mechanisms between these drugs and their associated diseases, but also for the development of drug therapy for various human diseases. This paper proposes a network-based approach to reveal the association between 52 human diseases and potential therapeutic drugs based on multiple types of data. The advantage of the approach is that it can obtain the global relevance features for each drug-disease pair in the network by the learning local and global consistency method (LLGC). Cross-validation tests results demonstrate the proposed approach can achieve better performance comparing with previous methods. More importantly, it provides a promising strategy to maximize the value of therapeutic drugs and offer safe and effective treatments for different diseases.