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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)

Abstract

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).

Notes

Acknowledgments

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

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

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

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