Abstract
Drug interactions represent adverse effects when employing two or multiple drugs together in treatments. Adverse effects are critical and may be deadly in medical practice. However, our understanding of drug interactions is far from complete. In the medical study on drug interaction, the prediction of potential drug interactions will help reducing the experimental efforts. In this paper, we extend a hinge-loss Markov Random Field Model and propose hybrid model of it and logistic regression. In the model, we combine multiple types of chemical and biological evidence to infer the interactions between drugs. Logistic regression is used to learn weights of those evidences. Experiments shows that our approach achieves better performance than the state-of-the-art approaches on both prediction accuracy and time efficiency.
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References
Klopmand, G.: Concepts and applications of molecular similarity. J. Comput. Chem. 13(4), 539–540 (1992)
Sridhar, D., Fakhraei, S., Getoor, L.: A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics 32(20), 3175–3182 (2016)
Chan, E., Tan, M., Xin, J., Sudarsanam, S., Johnson, D.E.: Interactions between traditional chinese medicines and western therapeutics. Curr. Opin. Drug Discov. Devel. 13(1), 50–65 (2010)
Obach, R.S.: Predicting drug-drug interactions from in vitro drug metabolism data: challenges and recent advances. Curr. Opin. Drug Discov. Devel. 12(1), 81–89 (2009)
Duke, J.D., et al.: Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions. PLos Comput. Biol. 8(8) 2012
Fokoue, A., Hassanzadeh, O., Sadoghi, M., Zhang, P.: Predicting drug-drug interactions through similarity-based link prediction over web data. In: proceedings of 25th International conference on companion world wide web, pp. 3–6 (2016)
Zhang, P., Wang, F., Hu, J., Sorrentino, R.: Label propagation prediction of drug-drug interactions based on clinical side effects. Sci. Rep. 5, 12339 (2015)
Bach, S., Huang, B., London, B., Getoor, L.: Hinge-loss markov random fields: convex inference for structured prediction. Uai (2013)
Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: Hinge-loss markov random fields and probabilistic soft logic. CoRR, vol. abs/1505.0, pp. 1–46 (2015)
Gottlieb, A., Stein, G.Y., Oron, Y., Ruppin, E., Sharan, R.: INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol. Syst. Biol. 8(592), 592 (2012)
Acknowledgements
The research is partially supported by GuangDong Science and Technology Project 2014A020221090 and the City University of Hong Kong Shenzhen Research Institute.
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Gu, H., Li, X. (2017). A Hybrid Markov Random Field Model for Drug Interaction Prediction. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_24
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DOI: https://doi.org/10.1007/978-3-319-67964-8_24
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