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ALADIN: A New Approach for Drug–Target Interaction Prediction

  • Krisztian Buza
  • Ladislav Peska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10535)

Abstract

Due to its pharmaceutical applications, one of the most prominent machine learning challenges in bioinformatics is the prediction of drug–target interactions. State-of-the-art approaches are based on various techniques, such as matrix factorization, restricted Boltzmann machines, network-based inference and bipartite local models (BLM). In this paper, we extend BLM by the incorporation of a hubness-aware regression technique coupled with an enhanced representation of drugs and targets in a multi-modal similarity space. Additionally, we propose to build a projection-based ensemble. Our Open image in new window technique (ALADIN) is evaluated on publicly available real-world drug–target interaction datasets. The results show that our approach statistically significantly outperforms BLM-NII, a recent version of BLM, as well as NetLapRLS and WNN-GIP.

Code related to this chapter is available at: https://github.com/lpeska/ALADIN

Data related to this chapter are available at: https://zenodo.org/record/556337#.WPiAzIVOIdV

Supplementary material is available at: http://www.biointelligence.hu/dti/

Keywords

Drug–target interaction prediction Bipartite local models ALADIN 

Notes

Acknowledgment

Ladislav Peska was supported by the Charles University grant P46.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Knowledge Discovery and Machine LearningRheinische Friedrich-Wilhelms-Universität BonnBonnGermany
  2. 2.Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

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