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Recommendation Techniques for Drug–Target Interaction Prediction and Drug Repositioning

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1415))

Abstract

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug–target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Città Universitaria, viale Andrea Doria 6, 95125, Catania, Italy

    Salvatore Alaimo, Rosalba Giugno & Alfredo Pulvirenti

Authors
  1. Salvatore Alaimo
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  2. Rosalba Giugno
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  3. Alfredo Pulvirenti
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Corresponding author

Correspondence to Alfredo Pulvirenti .

Editor information

Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

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Alaimo, S., Giugno, R., Pulvirenti, A. (2016). Recommendation Techniques for Drug–Target Interaction Prediction and Drug Repositioning. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_23

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  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_23

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3570-3

  • Online ISBN: 978-1-4939-3572-7

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