Affiliation:
1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Abstract
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.
Method:
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.
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
56 articles.
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