GRTR: Drug-Disease Association Prediction Based on Graph Regularized Transductive Regression on Heterogeneous Network
Autor: | Jiawei Luo, Qiu Xiao, Pingjian Ding, Qiao Zhu |
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Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
business.industry Computer science Machine learning computer.software_genre Graph regularization Regression 03 medical and health sciences Drug repositioning 030104 developmental biology 0302 clinical medicine System level DECIPHER Graph (abstract data type) Drug-disease Artificial intelligence business computer 030217 neurology & neurosurgery Heterogeneous network |
Zdroj: | Bioinformatics Research and Applications ISBN: 9783319949673 ISBRA |
Popis: | Computational drug repositioning helps to decipher the complex relations among drugs, targets, and diseases at a system level. However, most existing computational methods are biased towards known drugs-disease associations already verified by biological experiments. It is difficult to achieve excellent performance with sparse known drug-disease associations. In this article, we present a graph regularized transductive regression method (GRTR) to predict novel drug-disease associations. The proposed method first constructs a heterogeneous graph consisting of three interlinked sub-graphs including drugs, diseases and targets from multiple sources and adopts preliminary estimation of drug-related disease to initial unknown drug-disease associations for unlabeled drugs. Since the known drug-disease associations are sparse, graph regularized transductive regression is used to score and rank drug-disease associations iteratively. In the computational experiments, the proposed method achieves better performance than others in terms of AUC and AUPR. Moreover, the varying of parameters is shown to verify the importance of preliminary estimation in GRTR. Case studies on several selected drugs further confirm the practicality of our method in discovering potential indications for drugs. |
Databáze: | OpenAIRE |
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