Abstract
The identification of interactions between drugs and targets is a key area in drug research. Exploring targets can help identify potential side effects and toxicities for drugs, as well as new applications of existing drugs. Because of the enormous scale of biological dataset, most of the existing algorithms for drug-target mining are time-consuming. In this paper, we proposed an optimization algorithm called LSH-HeteSim to mine the drug-target interaction in heterogeneous biological networks, where the relationship between drugs and targets is various. It means drugs and targets are connected with complicated semantic path. In practice, the similarity measure used for semantic path is a path-dependent method, called HeteSim, which had been utilized in some previous studies of relevance search. Experiment results in real biological networks show that our algorithm can effectively predict drug-target interaction with the AUC measure achieving 0.943. Simultaneously, the running time of our algorithm is much less than the state-of-art methods.
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References
Hanzlik, R.P., Koen, Y.M., Theertham, B., et al.: The reactive metabolite target protein database (TPDB)—a web-accessible resource. BMC Bioinf. 8(1), 95 (2007)
Chan, S.Y., Loscalzo, J.: The emerging paradigm of network medicine in the study of human disease. Circul. Res. 111(3), 359–374 (2012)
Chen, L., Lu, J., Luo, X., et al.: Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections. Biochim. Biophys. Acta (BBA)-Proteins and Proteomics 1844, 207–213 (2013)
Campillos, M., Kuhn, M., Claude, G., et al.: Drug target identification using side-effect similarity. Science 321(5886), 263–266 (2008)
He, Z., Zhang, J., Shi, X.H., et al.: Predicting drug-target interaction networks based on functional groups and biological features. PloS one 5(3), e9603 (2010)
Chen, B., Ying, D., David, J.W.: Assessing drug target association using semantic linked data. PLoS Comput. Biol. 8(7), e1002574 (2012)
Yamanishi, Y., Kotera, M., Kanehisa, M., et al.: Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26(12), 246–254 (2010)
Fakhraei, S., Louiqa, L., Lise, G.: Drug-target interaction prediction for drug repurposing with probabilistic similarity logic. In: Proceedings of the 12th International Workshop on Data Mining in Bioinformatics. ACM (2013)
Sun, Y.Z., Han, J.W., Yan, X.F., et al.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB’11 (2011)
Shi, C., Kong, X.N., Yu, P.S., et al.: Relevance search in heterogeneous networks. In: Proceedings of the 15th International Conference on Extending Database Technology. ACM (2012)
Palma, G., Viadl, M.-E., Haag, L., et al.: Measuring relatedness between scientific entities in annotation datasets. In: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics. ACM (2013)
Getoor, L., Diehl, C.P.: Link mining: a survey. ACM SIGKDD Explor. Newslett. 7(2), 3–12 (2005)
Yu, H.Y., Braun, P., Yildirim, M.A., et al.: High-quality binary protein interaction map of the yeast interactome network. Science 322(5898), 104–110 (2008)
Datar, M., Immorlica, N., Indyk, P., et al.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry. ACM (2004)
Jegou, H., Matthijs, D., Cordelia, S.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2011)
Kishore, S.: Accelerated clustering through locality-sensitive hashing. Diss. Massachusetts Institute of Technology (2012)
Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of the thirty-fourth annual ACM symposium on Theory of computing. ACM (2002)
SLAP for Drug Target Prediction. http://cheminfov.informatics.indiana.edu:8080/slap
Saito, R., Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., Lotia, S., Pico, A.R., Bader, G.D., Ideker, T.: A travel guide to Cytoscape plugins. Nat. Methods 9(11), 1069–1076 (2012)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2005)
Zheng, G., Wang, H., Wei, C., Li, Y.: iGepros: an integrated gene and protein annotation sever for biological nature exploration. BMC Bioinf. 12(Suppl 14), S6 (2011)
Lv, Q., Josephson, W., Wang, Z., et al.: Multi-probe LSH: efficient indexing for high-dimensional similarity search. VLDB Endowment, pp. 950–961 (2007)
Acknowledgment
The work was supported in part by the National Natural Science Foundation Project of China under Grant. No. 61170096 and Research Program of Shanghai Science and Technology Committee under Grant. No. 12511502403. The authors gratefully acknowledge the support of SA-SIBS Scholarship Program.
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Li, C., Sun, J., Xiong, Y., Zheng, G. (2014). An Efficient Drug-Target Interaction Mining Algorithm in Heterogeneous Biological Networks. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_7
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