Abstract
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.
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References
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems \(26\) (2013)
Cheng, F., Zhao, Z.: Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J. Am. Med. Inform. Assoc. 21(e2), e278–e286 (2014)
Fishman, D.A., Liu, Y., Ellerbroek, S.M., Stack, M.S.: Lysophosphatidic acid promotes matrix metalloproteinase (MMP) activation and MMP-dependent invasion in ovarian cancer cells. Cancer Res. 61(7), 3194–3199 (2001)
García-Durán, A., Niepert, M.: KBLRN: end-to-end learning of knowledge base representations with latent, relational, and numerical features. In: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (2018)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)
Kuhn, M., Letunic, I., Jensen, L.J., Bork, P.: The SIDER database of drugs and side effects. Nucl. Acids Res. 44(D1), D1075–D1079 (2016)
Manicone, A.M., McGuire, J.K.: Matrix metalloproteinases as modulators of inflammation. Semin. Cell Dev. Biol. 19(1), 34–41 (2008)
Munshi, H.G., Wu, Y.I., Ariztia, E.V., Stack, M.S.: Calcium regulation of matrix metalloproteinase-mediated migration in oral squamous cell carcinoma cells. J. Biol. Chem. 277(44), 41480–41488 (2002)
Sridhar, D., Fakhraei, S., Getoor, L.: A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics 32(20), 3175–3182 (2016)
Szklarczyk, D., Santos, A., von Mering, C., Jensen, L.J., Bork, P., Kuhn, M.: STITCH 5: augmenting protein-checical interaction networks with tissue and affinity data. Nucl. Acids Res. 44, D380–D384 (2016)
Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31 (2012)
Yang, B., tau Yih, S.W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd International Conference on Learning Representations (2015)
Zhang, W., Chen, Y., Liu, F., Luo, F., Tian, G., Li, X.: Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 18, 18 (2017)
Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), 457–466 (2018)
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Malone, B., García-Durán, A., Niepert, M. (2019). Knowledge Graph Completion to Predict Polypharmacy Side Effects. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_14
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