GRTR: Drug-Disease Association Prediction Based on Graph Regularized Transductive Regression on Heterogeneous Network
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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.
KeywordsTransductive regression Drug repositioning Drug-disease association Graph regularization Heterogeneous network
This work has been supported by the National Natural Science Foundation of China (Grant No. 61572180).
- 2.Wang, W., Yang, S., Li, J.: Drug target predictions based on heterogeneous graph inference. Biocomputing 2013, pp. 53–64. World scientific, Kohala Coast, Hawaii, USA (2012)Google Scholar
- 12.Wan, M., Ouyang, Y., Kaplan, L., Han, J.: Graph regularized meta-path based transductive regression in heterogeneous information network. In: Proceedings of the 2015 SIAM International Conference on Data Mining 2015, pp. 918–926 (2015)Google Scholar
- 16.Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F., Furlong, L.I.: DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45(D1), D833–D839 (2017)CrossRefGoogle Scholar
- 18.Keshava Prasad, T.S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A., Balakrishnan, L., Marimuthu, A., Banerjee, S., Somanathan, D.S., Sebastian, A., Rani, S., Ray, S., Harrys Kishore, C.J., Kanth, S., Ahmed, M., Kashyap, M.K., Mohmood, R., Ramachandra, Y.L., Krishna, V., Rahiman, B.A., Mohan, S., Ranganathan, P., Ramabadran, S., Chaerkady, R., Pandey, A.: Human protein reference database—2009 update. Nucleic Acids Res. 37(suppl_1), D767–D772 (2009)CrossRefGoogle Scholar
- 21.Tanimoto, T.T.: Elementary mathematical theory of classification and prediction. IBM Internal report, pp. 1–10 (1958)Google Scholar
- 23.Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025–1033. ACM, Chicago, Illinois, USA (2013)Google Scholar
- 28.Coves, M.J., Gomis, R., Goday, A., Casamitjana, R., Rivera, F., Vilardell, E.: Antihypertensive treatment with guanfacine in patients with type II diabetes mellitus. Med Clin (Barc) 88(8), 315–317 (1987)Google Scholar
- 29.Ahmad, A.: Carvedilol can replace insulin in the treatment of type 2 diabetes mellitus. J. Diab. Metab. 8(2), (2017)Google Scholar