Abstract
The identification of drug-target interactions plays a crucial role in drug discovery and design. However, capturing interactions between drugs and targets via traditional biochemical experiments is an extremely laborious, expensive and time-consuming procedure. Therefore, the use of computational methods for predicting potential interactions to guide the experimental verification has attracted a lot of attention. In this paper, we propose a new algorithm, named Laplacian Regularized Schatten-p Norm Minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets. First, we take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten-p norm minimization model to improve prediction performance in the new drug/target cases by combining the loss function with a Laplacian regularization term. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers (ADMM) algorithm. Performance evaluations on benchmark datasets show that LRSpNM achieves better and more robust performance than five state-of-the-art drug-target interaction prediction algorithms. In addition, we conduct case study in practical applications, which also illustrates the effectiveness of our proposed method.
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References
Wishart, D.S., et al.: Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36(Database issue), D901–D906 (2008)
Apweiler, R., et al.: UniProt: the universal protein knowledgebase. Nucleic Acids Res. 32(Database issue), D115–D119 (2004)
Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397–2403 (2009)
Xia, Z., Wu, L.Y., Zhou, X., Wong, S.T.: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol. 4(2), S6 (2010)
Van Laarhoven, T., Marchiori, E.: Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PLoS One 8(6), e66952 (2013)
Gönen, M.: Predicting drug-target interactions from chemical and genomic kernels using bayesian matrix factorization. Bioinformatics 28(18), 2304–2310 (2012)
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 (2013)
Liu, Y., Wu, M., Miao, C., Zhao, P., Li, X.L.: Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS Comput. Biol. 12(2), e1004760 (2016)
Fan, X., Hong, Y., Liu, X., Zhang, Y., Xie, M.: Neighborhood constraint matrix completion for drug-target interaction prediction. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10937, pp. 348–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93034-3_28
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232–i240 (2008)
Hattori, M., Okuno, Y., Goto, S., Kanehisa, M.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125(39), 11853–11865 (2003)
Smith, T.F., Waterman, M.S., et al.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Fazel, M.: Matrix rank minimization with applications. Ph.D. thesis, Stanford University (2002)
Nie, F., Huang, H., Ding, C.: Low-rank matrix recovery via efficient Schatten p-norm minimization. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (2012)
Nie, F., Wang, H., Huang, H., Ding, C.: Joint schatten \(p\)-norm and \(\ell _{p}\)-norm robust matrix comletion for missing value recovery. Knowl. Inf. Syst. 42(3), 525–544 (2015)
Chen, C., He, B., Yuan, X.: Matrix completion via an alternating direction method. IMA J. Numer. Anal. 32(1), 227–245 (2012)
Yang, M., Luo, H., Li, Y., Wang, J.: Drug repositioning based on bounded nuclear norm regularization. Bioinformatics 35(14), i455–i463 (2019)
Bartels, R.H., Stewart, G.W.: Solution of the matrix equation AX+XB=C [F4]. Commun. ACM 15(9), 820–826 (1972)
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., Tanabe, M.: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40(D1), D109–D114 (2012)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant No. 61972423), the Graduate Research Innovation Project of Hunan (Grant No. CX20190125), Hunan Provincial Science and technology Program (No. 2018wk4001), and 111Project (No. B18059).
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Wu, G., Yang, M., Li, Y., Wang, J. (2020). De novo Prediction of Drug-Target Interaction via Laplacian Regularized Schatten-p Norm Minimization. In: Cai, Z., Mandoiu, I., Narasimhan, G., Skums, P., Guo, X. (eds) Bioinformatics Research and Applications. ISBRA 2020. Lecture Notes in Computer Science(), vol 12304. Springer, Cham. https://doi.org/10.1007/978-3-030-57821-3_14
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DOI: https://doi.org/10.1007/978-3-030-57821-3_14
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