Skip to main content
SpringerLink
Log in

MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

Included in the following conference series:

Abstract

Predicting the relationships between drugs and targets is a crucial step in the course of drug discovery and development. Computational prediction of associations between drugs and targets greatly enhances the probability of finding new interactions by reducing the cost of in vitro experiments. In this paper, a Meta-path-based Representation Learning model, namely MRLDTI, is proposed to predict unknown DTIs. Specifically, we first design a random walk strategy with a meta-path to collect the biological relations of drugs and targets. Then, the representations of drugs and targets are captured by a heterogeneous skip-gram algorithm. Finally, a machine learning classifier is employed by MRLDTI to discover novel DTIs. Experimental results indicate that MRLDTI performs better than several state-of-the-art models under ten-fold cross-validation on the gold standard dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Su, X., Hu, L., You, Z., Hu, P., Wang, L., Zhao, B.: A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2. Brief. Bioinform. 23, bbab526 (2022)

    Google Scholar 

  2. Zhao, B.-W., Hu, L., You, Z.-H., Wang, L., Su, X.-R.: HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks. Brief. Bioinform. 23, bbab515 (2022)

    Google Scholar 

  3. Hu, L., Chan, K.C.: A density-based clustering approach for identifying overlapping protein complexes with functional preferences. BMC Bioinform. 16, 1–16 (2015)

    Article  Google Scholar 

  4. Hu, L., Chan, K.C.: Extracting coevolutionary features from protein sequences for predicting protein-protein interactions. IEEE/ACM Trans. Comput. Biol. Bioinf. 14, 155–166 (2016)

    Article  Google Scholar 

  5. You, Z.-H., Li, X., Chan, K.C.: An improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers. Neurocomputing 228, 277–282 (2017)

    Article  Google Scholar 

  6. Ezzat, A., Wu, M., Li, X.-L., Kwoh, C.-K.J.B.i.b.: Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey, 20, 1337–1357 (2019)

    Google Scholar 

  7. Hu, L., Yuan, X., Liu, X., Xiong, S., Luo, X.: Efficiently detecting protein complexes from protein interaction networks via alternating direction method of multipliers. IEEE/ACM Trans. Comput. Biol. Bioinf. 16, 1922–1935 (2018)

    Article  Google Scholar 

  8. Guo, Z.-H., Yi, H.-C., You, Z.-H.: Construction and comprehensive analysis of a molecular association network via lncRNA–miRNA–disease–drug–protein graph. Cells 8, 866 (2019)

    Article  Google Scholar 

  9. Hu, L., Chan, K.C., Yuan, X., Xiong, S.: A variational Bayesian framework for cluster analysis in a complex network. IEEE Trans. Knowl. Data Eng. 32, 2115–2128 (2019)

    Article  Google Scholar 

  10. Wang, L., You, Z.-H., Li, Y.-M., Zheng, K., Huang, Y.-A.: GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol. 16, e1007568 (2020)

    Article  Google Scholar 

  11. Yi, H.-C., You, Z.-H., Wang, M.-N., Guo, Z.-H., Wang, Y.-B., Zhou, J.-R.: RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information. BMC Bioinform. 21, 1–10 (2020)

    Article  Google Scholar 

  12. Wang, L., You, Z.-H., Huang, D.-S., Zhou, F.: Combining high speed ELM learning with a deep convolutional neural network feature encoding for predicting protein-RNA interactions. IEEE/ACM Trans. Comput. Biol. Bioinf. 17, 972–980 (2018)

    Article  Google Scholar 

  13. Hu, L., Wang, X., Huang, Y., Hu, P., You, Z.-H.: A novel network-based algorithm for predicting protein-protein interactions using gene ontology. Front. Microbiol. 2441 (2021)

    Google Scholar 

  14. Pan, X., Hu, L., Hu, P., You, Z.-H.: Identifying protein complexes from protein-protein interaction networks based on fuzzy clustering and GO semantic information. IEEE/ACM Trans. Comput. Biol. Bioinform. (2021)

    Google Scholar 

  15. Hu, L., Zhang, J., Pan, X., Yan, H., You, Z.-H.: HiSCF: leveraging higher-order structures for clustering analysis in biological networks. Bioinformatics 37, 542–550 (2021)

    Article  Google Scholar 

  16. Li, Z., Hu, L., Tang, Z., Zhao, C.: Predicting HIV-1 protease cleavage sites with positive-unlabeled learning. Front. Genet. 12, 658078 (2021)

    Article  Google Scholar 

  17. Hu, L., Yang, S., Luo, X., Yuan, H., Sedraoui, K., Zhou, M.: A distributed framework for large-scale protein-protein interaction data analysis and prediction using mapreduce. IEEE/CAA J. Automatica Sinica 9, 160–172 (2021)

    Article  Google Scholar 

  18. Luo, Y., et al.: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8, 1–13 (2017)

    Article  Google Scholar 

  19. Hu, P., Huang, Y., You, Z., Li, S., Chan, K.C.C., Leung, H., Hu, L.: Learning from deep representations of multiple networks for predicting drug–target interactions. In: Huang, D.-S., Jo, K.-H., Huang, Z.-K. (eds.) ICIC 2019. LNCS, vol. 11644, pp. 151–161. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26969-2_14

    Chapter  Google Scholar 

  20. Wan, F., Hong, L., Xiao, A., Jiang, T., Zeng, J.: NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions. Bioinformatics 35, 104–111 (2019)

    Article  Google Scholar 

  21. Hu, A.L., Chan, K.C.: Utilizing both topological and attribute information for protein complex identification in ppi networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 10, 780–792 (2013)

    Article  Google Scholar 

  22. Hu, L., Chan, K.C.: Fuzzy clustering in a complex network based on content relevance and link structures. IEEE Trans. Fuzzy Syst. 24, 456–470 (2015)

    Article  Google Scholar 

  23. Xing, W., et al.: A gene–phenotype relationship extraction pipeline from the biomedical literature using a representation learning approach. Bioinformatics 34, i386–i394 (2018)

    Article  Google Scholar 

  24. Hu, P., Huang, Y.-A., Chan, K.C., You, Z.-H.: Learning multimodal networks from heterogeneous data for prediction of lncRNA-miRNA interactions. IEEE/ACM Trans. Comput. Biol. Bioinform. (2019)

    Google Scholar 

  25. Jiang, H.-J., You, Z.-H., Hu, L., Guo, Z.-H., Ji, B.-Y., Wong, L.: A highly efficient biomolecular network representation model for predicting drug-disease associations. In: Huang, D.-S., Premaratne, P. (eds.) ICIC 2020. LNCS (LNAI), vol. 12465, pp. 271–279. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60796-8_23

    Chapter  Google Scholar 

  26. Hu, L., Yang, S., Luo, X., Zhou, M.: An algorithm of inductively identifying clusters from attributed graphs. IEEE Trans. Big Data (2020)

    Google Scholar 

  27. Wang, L., You, Z.-H., Li, J.-Q., Huang, Y.-A.: IMS-CDA: prediction of CircRNA-disease associations from the integration of multisource similarity information with deep stacked autoencoder model. IEEE Trans. Cybern. 51, 5522–5531 (2020)

    Article  Google Scholar 

  28. Guo, Z.-H., et al.: MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm. Brief. Bioinform. 22, 2085–2095 (2021)

    Article  Google Scholar 

  29. Wang, L., You, Z.-H., Huang, Y.-A., Huang, D.-S., Chan, K.C.: An efficient approach based on multi-sources information to predict circRNA–disease associations using deep convolutional neural network. Bioinformatics 36, 4038–4046 (2020)

    Article  Google Scholar 

  30. Hu, L., Zhang, J., Pan, X., Luo, X., Yuan, H.: An effective link-based clustering algorithm for detecting overlapping protein complexes in protein-protein interaction networks. IEEE Trans. Netw. Sci. Eng. 8, 3275–3289 (2021)

    Article  Google Scholar 

  31. Wang, L., You, Z.-H., Zhou, X., Yan, X., Li, H.-Y., Huang, Y.-A.: NMFCDA: combining randomization-based neural network with non-negative matrix factorization for predicting CircRNA-disease association. Appl. Soft Comput. 110, 107629 (2021)

    Article  Google Scholar 

  32. Hu, L., Zhao, B.-W., Yang, S., Luo, X., Zhou, M.: Predicting large-scale protein-protein interactions by extracting coevolutionary patterns with MapReduce paradigm. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 939–944. IEEE (2021)

    Google Scholar 

  33. Zhao, B.-W., You, Z.-H., Hu, L., Wong, L., Ji, B.-Y., Zhang, P.: A multi-graph deep learning model for predicting drug-disease associations. In: Huang, D.-S., Jo, K.-H., Li, J., Gribova, V., Premaratne, P. (eds.) ICIC 2021. LNCS (LNAI), vol. 12838, pp. 580–590. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84532-2_52

    Chapter  Google Scholar 

  34. Hu, L., Wang, X., Huang, Y.-A., Hu, P., You, Z.-H.: A survey on computational models for predicting protein–protein interactions. Brief. Bioinform. 22, bbab036 (2021)

    Google Scholar 

  35. Su, X.-R., You, Z.-H., Yi, H.-C., Zhao, B.-W.: Detection of drug-drug interactions through knowledge graph integrating multi-attention with capsule network. In: Huang, D.-S., Jo, K.-H., Li, J., Gribova, V., Premaratne, P. (eds.) ICIC 2021. LNCS (LNAI), vol. 12838, pp. 423–432. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84532-2_38

    Chapter  Google Scholar 

  36. Zhao, B.-W., You, Z.-H., Wong, L., Zhang, P., Li, H.-Y., Wang, L.J.F.i.G.: MGRL: predicting drug-disease associations based on multi-graph representation learning. Front. Genet. 12, 491 (2021)

    Google Scholar 

  37. Su, X., et al.: SANE: a sequence combined attentive network embedding model for COVID-19 drug repositioning. Appl. Soft Comput. 111, 107831 (2021)

    Article  Google Scholar 

  38. Zhang, H.-Y., Wang, L., You, Z.-H., Hu, L., Zhao, B.-W., Li, Z.-W., Li, Y.-M.: iGRLCDA: identifying circRNA–disease association based on graph representation learning. Brief. Bioinform. 23, bbac083 (2022)

    Google Scholar 

  39. Su, X.-R., Huang, D.-S., Wang, L., Wong, L., Ji, B.-Y., Zhao, B.-W.: Biomedical knowledge graph embedding with capsule network for multi-label drug-drug interaction prediction. IEEE Trans. Knowl. Data Eng. (2022)

    Google Scholar 

  40. Chen, Z.-H., You, Z.-H., Guo, Z.-H., Yi, H.-C., Luo, G.-X., Wang, Y.-B.: Prediction of drug-target interactions from multi-molecular network based on deep walk embedding model. Front. Bioeng. Biotechnol. 8, 338 (2020)

    Article  Google Scholar 

  41. Zhao, B.-W., et al.: A novel method to predict drug-target interactions based on large-scale graph representation learning. Cancers 13, 2111 (2021)

    Article  Google Scholar 

  42. Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)

    Google Scholar 

  43. Wang, L., You, Z.-H., Huang, D.-S., Li, J.-Q.: MGRCDA: metagraph recommendation method for predicting CircRNA-disease association. IEEE Trans. Cybern. (2021)

    Google Scholar 

  44. Li, J., Wang, J., Lv, H., Zhang, Z., Wang, Z.: IMCHGAN: inductive matrix completion with heterogeneous graph attention networks for drug-target interactions prediction. IEEE/ACM Trans. Comput. Biol. Bioinf. 19, 655–665 (2021)

    Article  Google Scholar 

  45. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)

    Google Scholar 

  46. Wang, W., Yang, S., Zhang, X., Li, J.: Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 30, 2923–2930 (2014)

    Article  Google Scholar 

  47. Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025–1033. 2013)

    Google Scholar 

  48. Yan, X.-Y., Zhang, S.-W., Zhang, S.-Y.: Prediction of drug–target interaction by label propagation with mutual interaction information derived from heterogeneous network. Mol. BioSyst. 12, 520–531 (2016)

    Article  Google Scholar 

  49. Guo, Z.-H., You, Z.-H., Yi, H.-C.: Integrative construction and analysis of molecular association network in human cells by fusing node attribute and behavior information. Mol. Therapy-Nucl. Acids 19, 498–506 (2020)

    Article  Google Scholar 

  50. Yi, H.-C., You, Z.-H., Guo, Z.-H., Huang, D.-S., Chan, K.C.: Learning representation of molecules in association network for predicting intermolecular associations. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020)

    Google Scholar 

  51. Guo, Z.-H., You, Z.-H., Wang, Y.-B., Huang, D.-S., Yi, H.-C., Chen, Z.-H.: Bioentity2vec: attribute-and behavior-driven representation for predicting multi-type relationships between bioentities. GigaScience9, giaa032 (2020)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under grant 2021D01D05, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences, in part by the National Natural Science Foundation of China, under Grants 61702444, 62002297, 61902342, in part by Awardee of the NSFC Excellent Young Scholars Program, under Grant 61722212, and in part by the Tianshan youth - Excellent Youth, under Grant 2019Q029.

Author information

Authors and Affiliations

  1. The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Bo-Wei Zhao, Lun Hu, Peng-Wei Hu, Xiao-Rui Su & Dong-Xu Li

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Bo-Wei Zhao, Lun Hu, Peng-Wei Hu, Xiao-Rui Su & Dong-Xu Li

  3. Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China

    Bo-Wei Zhao, Lun Hu, Peng-Wei Hu, Xiao-Rui Su & Dong-Xu Li

  4. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China

    Zhu-Hong You

  5. College of Computer Science and Engineering, Shenzhen University, Shenzhen, 518060, China

    Zhan-Heng Chen

  6. Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China

    Ping Zhang

Authors
  1. Bo-Wei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng-Wei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhu-Hong You
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiao-Rui Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dong-Xu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhan-Heng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Lun Hu or Zhu-Hong You .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, BW. et al. (2022). MRLDTI: A Meta-path-Based Representation Learning Model for Drug-Target Interaction Prediction. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-13829-4_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation