Identification of Drug-Disease Associations by Using Multiple Drug and Disease Networks

Background: Drug repositioning is a new research area in drug development. It aims to discover novel therapeutic uses of existing drugs. It could accelerate the process of designing novel drugs for some diseases and considerably decrease the cost. The traditional method to determine novel therapeutic uses of an existing drug is quite laborious. It is alternative to design computational methods to overcome such defect. Objective: This study aims to propose a novel model for the identification of drug-disease associations. Methods: Twelve drug networks and three disease networks were built, which were fed into a powerful network-embedding algorithm called Mashup to produce informative drug and disease features. These features were combined to represent each drug-disease association. Classic classification algorithm, random forest, was used to build the model. Results: Tenfold cross-validation results indicated that the MCC, AUROC, and AUPR were 0.7156, 0.9280, and 0.9191, respectively. Conclusion: The proposed model showed good performance. Some tests indicated that a small dimension of drug features and a large dimension of disease features were beneficial for constructing the model. Moreover, the model was quite robust even if some drug or disease properties were not available.

Automated summary

Drug repositioning is a method to discover new therapeutic uses of existing drugs. This study proposed a novel model to identify drug-disease associations by building drug networks, disease networks, and generating features using the Mashup algorithm, and then building the model with the random forest classification algorithm. The model showed good performance with some tests indicating optimal dimensions of drug and disease features for construction.