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Predicting Unknown Interactions Between Known Drugs and Targets via Matrix Completion

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Advances in Knowledge Discovery and Data Mining (PAKDD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9651))

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Abstract

Drug-target interactions map patterns, associations and relationships between drugs and target proteins. Identifying interactions between drug and target is critical in drug discovery, but biochemically validating these interactions are both laborious and expensive. In this paper, we propose a novel interaction profiles based method to predict potential drug-target interactions by using matrix completion. Our method first arranges the drug-target interactions in a matrix, whose entries include interaction pairs, non-interaction pairs and undetermined pairs, and finds its approximation matrix which contains the predicted values at undetermined positions. Then our method learns an approximation matrix by minimizing the distance between the drug-target interaction matrix and its approximation subject that the values in the observed positions equal to the known interactions at the corresponding positions. As a consequence, our method can directly predict new potential interactions according to the high values at the undetermined positions. We evaluated our method by comparing against five counterpart methods on “gold standard” datasets. Our method outperforms the counterparts, and achieves high AUC and \(F_1\)-score on enzyme, ion channel, GPCR, nuclear receptor and integrated datasets, respectively. We showed the intelligibility of our method by validating some predicted interactions in both DrugBank and KEGG databases.

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Acknowledgments

This work was supported by The National Natural Science Foundation of China (under grant No. U1435222 and No. 61502515). And this work was also supported in part by grants from 973 project 2013CB329006, RGC under the contract CERG 16212714.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Qing Liao & Qian Zhang

  2. College of Computer, National University of Defense Technology, Changsha, China

    Naiyang Guan & Chengkun Wu

Authors
  1. Qing Liao
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  2. Naiyang Guan
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  3. Chengkun Wu
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  4. Qian Zhang
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Corresponding author

Correspondence to Qing Liao .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  2. The University of Texas at Dallas, Richardson, Texas, USA

    Latifur Khan

  3. Osaka University, Osaka, Japan

    Takashi Washio

  4. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  5. Shenzhen University, Shenzhen, China

    Joshua Zhexue Huang

  6. Massey University, Auckland, New Zealand

    Ruili Wang

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Liao, Q., Guan, N., Wu, C., Zhang, Q. (2016). Predicting Unknown Interactions Between Known Drugs and Targets via Matrix Completion. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9651. Springer, Cham. https://doi.org/10.1007/978-3-319-31753-3_47

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  • DOI: https://doi.org/10.1007/978-3-319-31753-3_47

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