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GRTR: Drug-Disease Association Prediction Based on Graph Regularized Transductive Regression on Heterogeneous Network

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Bioinformatics Research and Applications (ISBRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10847))

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Abstract

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.

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Acknowledgment

This work has been supported by the National Natural Science Foundation of China (Grant No. 61572180).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Collaboration and Innovation Center for Digital Chinese Medicine in Hunan Province, Hunan University, Changsha, 410082, China

    Qiao Zhu, Jiawei Luo, Pingjian Ding & Qiu Xiao

Authors
  1. Qiao Zhu
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  2. Jiawei Luo
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  3. Pingjian Ding
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  4. Qiu Xiao
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Corresponding author

Correspondence to Jiawei Luo .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Fa Zhang

  2. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

  3. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  4. Chinese Academy of Sciences, Beijing, China

    Shihua Zhang

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Zhu, Q., Luo, J., Ding, P., Xiao, Q. (2018). GRTR: Drug-Disease Association Prediction Based on Graph Regularized Transductive Regression on Heterogeneous Network. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-94968-0_2

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