Boosting Collaborative Filters for Drug-Target Interaction Prediction

  • Cristian Orellana M.Email author
  • Ricardo Ñanculef
  • Carlos Valle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


In-silico prediction of interactions between drugs and proteins has become a crucial step in pharmaceutical sciences to reduce the time and cost required for drug discovery and repositioning. Even if the problem may be approached using standard recommendation algorithms, the accurate prediction of unknown drug-target interactions has shown to be very challenging due to the relatively small number of drugs with information of their target proteins and viceversa. This issue has been recently circumvent using regularization methods that actively exploit prior knowledge regarding drug similarities and target similarities. In this paper, we show that an additional improvement in terms of accuracy can be obtained using an ensemble approach which learns to combine multiple regularized filters for prediction. Our experiments on eight drug-protein interaction datasets show that most of the time this method outperforms a single predictor and other recommender systems based on multiple filters but not specialized to the drug-target interaction prediction task.


Drug-target interaction prediction Collaborative filtering Ensemble methods 



This research was partially supported by PIIC-2018 program of DGIP from the Federico Santa María Technical University.


  1. 1.
    Ban, T., Ohue, M., Akiyama, Y.: Efficient hyperparameter optimization by using Bayesian optimization for drug-target interaction prediction. In: IEEE 7th ICCABS, pp. 1–6, October 2017Google Scholar
  2. 2.
    Cobanoglu, M.C., Liu, C., Hu, F., Oltvai, Z.N., Bahar, I.: Predicting drug-target interactions using probabilistic matrix factorization. J. Chem. Inf. Model. 53(12), 3399–3409 (2013)CrossRefGoogle Scholar
  3. 3.
    Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S.: Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Briefings Bioinform. 15(5), 734 (2014). Scholar
  4. 4.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 38(2), 337–407 (2000)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gönen, M.: Predicting drug-target interactions from chemical and genomic kernels using bayesian matrix factorization. Bioinformatics 28(18), 2304–2310 (2012)CrossRefGoogle Scholar
  6. 6.
    Johnson, C.C.: Logistic matrix factorization for implicit feedback data. In: Advances in Neural Information Processing Systems 27 (2014)Google Scholar
  7. 7.
    Keum, J., Nam, H.: Self-BLM: prediction of drug-target interactions via self-training SVM. PLOS ONE 12(2), 1–16 (2017). Scholar
  8. 8.
    Li, Z., et al.: In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci. Rep. 7(1), 11174 (2017)CrossRefGoogle Scholar
  9. 9.
    Liu, Y., Wu, M., Miao, C., Zhao, P., Li, X.L.: Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLOS Comput. Biol. 12(2), 1–26 (2016). Scholar
  10. 10.
    Rayhan, F., Ahmed, S., Shatabda, S., Farid, D.M., Mousavian, Z., Dehzangi, A., Rahman, M.S.: iDTI-ESBoost: identification of drug target interaction using evolutionary and structural features with boosting. Sci. Rep. 7(1), 17731 (2017)CrossRefGoogle Scholar
  11. 11.
    Smola, A.J., Schölkopf, B.: Learning with kernels, vol. 4. Citeseer (1998)Google Scholar
  12. 12.
    Tsai, C.F., Hung, C.: Cluster ensembles in collaborative filtering recommendation. Appl. Soft Comput. 12(4), 1417–1425 (2012)CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Sun, H., Zhang, R.: AdaMF: adaptive boosting matrix factorization for recommender system. In: Li, F., Li, G., Hwang, S., Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 43–54. Springer, Cham (2014). Scholar
  14. 14.
    Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232 (2008). Scholar
  15. 15.
    Yuan, Q., Gao, J., Wu, D., Zhang, S., Mamitsuka, H., Zhu, S.: Druge-rank: improving drugtarget interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics 32(12), i18–i27 (2016)CrossRefGoogle Scholar
  16. 16.
    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, pp. 1025–1033. ACM (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cristian Orellana M.
    • 1
    Email author
  • Ricardo Ñanculef
    • 1
  • Carlos Valle
    • 2
  1. 1.Department of InformaticsFederico Santa María Technical UniversityValparaísoChile
  2. 2.Department of Computer Science and InformaticsUniversity of Playa AnchaValparaísoChile

Personalised recommendations