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A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Open access
Luo, Yunan, Zhao, Xinbin, Zhou, Jingtian, Yang, Jinglin, Zhang, Yanqing, Kuang, Wenhua, Peng, Jian, Chen, Ligong, Zeng, Jianyang·2017·Nature Communications

Published version of a preprint

Abstract

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.

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Research works printed but not linked: 4

  1. Yang, F., Xu, J. & Zeng, J. in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 148 (NIH Public Access, 2014).
  2. Tong, H., Faloutsos, C. & Pan, J.-Y. In Proceedings of the Sixth International Conference on Data Mining, 613–622 (IEEE Computer Society, 2006).
  3. Sato, K. et al. Telmisartan ameliorates inflammatory responses in SHR-SR after tMCAO. J. Stroke Cerebrovasc. Dis. 23, 2511–2519 (2014). · doi:10.1016/j.jstrokecerebrovasdis.2014.02.019
  4. Töyräs, A., Ollikainen, J., Taskinen, M. & Mönkkönen, J. Inhibition of mevalonate pathway is involved in alendronate-induced cell growth inhibition, but not in cytokine secretion from macrophages in vitro. Eur. J. Pharmaceut. Sci. 19, 223–230 (2003). · doi:10.1016/s0928-0987(03)00108-8

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Metadata

Authors
Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L., & Zeng, J.
Venue
Nature Communications
Published
2017-09-18
Volume
8
Issue
1
Pages
573-573
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2753953057
Cited by
916
References
97
FWCI
39.49
Type
journal article
Language
en
Open access
gold
Licence

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CoChat. (2026). Citation check: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. https://app.cochat.ai/papers/10.1038/s41467-017-00680-8
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Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L., & Zeng, J. (2017). A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications. https://doi.org/10.1038/s41467-017-00680-8

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