Positive Unlabeled Link Prediction via Transfer Learning for Gene Network Reconstruction

  • Paolo Mignone
  • Gianvito PioEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11177)


Transfer learning can be employed to leverage knowledge from a source domain in order to better solve tasks in a target domain, where the available data is exiguous. While most of the previous papers work in the supervised setting, we study the more challenging case of positive-unlabeled transfer learning, where few positive labeled instances are available for both the source and the target domains. Specifically, we focus on the link prediction task on network data, where we consider known existing links as positive labeled data and all the possible remaining links as unlabeled data. In many real applications (e.g., in bioinformatics), this usually leads to few positive labeled data and a huge amount of unlabeled data. The transfer learning method proposed in this paper exploits the unlabeled data and the knowledge of a source network in order to improve the reconstruction of a target network. Experiments, conducted in the biological field, showed the effectiveness of the proposed approach with respect to the considered baselines, when exploiting the Mus Musculus gene network (source) to improve the reconstruction of the Homo Sapiens Sapiens gene network (target).



We would like to acknowledge the European project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (ICT-2013-612944).


  1. 1.
    Platt, J.C.: Probabilistic outputs for support vector machine and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers (1999)Google Scholar
  2. 2.
    Ceci, M., Pio, G., Kuzmanovski, V., Džeroski, S.: Semi-supervised multi-view learning for gene network reconstruction. Plos One, 10(12), e0144031 (2015)CrossRefGoogle Scholar
  3. 3.
    Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proceedings of ICML, pp. 193–200 (2007)Google Scholar
  4. 4.
    Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of ACM SIGKDD, pp. 213–220 (2008)Google Scholar
  5. 5.
    Jowkar, G., Mansoori, E.: Perceptron ensemble of graph-based positive unlabeled learning for disease gene identification. Comput. Biol. Chem. 64, 263–270 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Levatic, J., Ceci, M., Kocev, D., Dzeroski, S.: Self-training for multi-target regression with tree ensembles. Knowl. Based Syst. 123, 41–60 (2017)CrossRefGoogle Scholar
  7. 7.
    Levatic, J., Kocev, D., Ceci, M., Dzeroski, S.: Semi-supervised trees for multi-target regression. Inf. Sci. 450, 109–127 (2018)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially supervised classification of text documents. In: Proceedings of ICML, pp. 387–394 (2002)Google Scholar
  9. 9.
    Marbach, D., et al.: Wisdom of crowds for robust gene network inference. Nat. Meth. 9(8), 796–804 (2016)CrossRefGoogle Scholar
  10. 10.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  11. 11.
    Pan, S.J., Zheng, V.W., Yang, Q., Hu, D.H.: Transfer learning for wifi-based indoor localization. In: Workshop on Transfer Learning for Complex Task AAAI (2008)Google Scholar
  12. 12.
    Pio, G., Ceci, M., Malerba, D., D’Elia, D.: ComiRNet:a web-based system for the analysis of miRNA-gene regulatory networks. BMC Bioinform. 16(S-9), S7 (2015)CrossRefGoogle Scholar
  13. 13.
    Pio, G., Malerba, D., D’Elia, D., Ceci, M.: Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach. BMC Bioinform. 15(S-1), S4 (2014)CrossRefGoogle Scholar
  14. 14.
    Weiss, K.R., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3, 9 (2016)CrossRefGoogle Scholar
  15. 15.
    Yang, X., Song, Q., Wand, Y.: A weighted support vector machine for data classification. Int. J. Pattern Recogn. 21, 961–976 (2007)CrossRefGoogle Scholar
  16. 16.
    Zhang, B., Zuo, W.: Learning from positive and unlabeled examples: a survey. In: ISIP/WMWA, pp. 650–654 (2008)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly

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