Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
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.
Department of Mathematics and Computer Science, University of Catania, Città Universitaria, viale Andrea Doria 6, 95125, Catania, Italy.
Salvatore
S
Alaimo
Department of Mathematics and Computer Science, University of Catania, Città Universitaria, viale Andrea Doria 6, 95125, Catania, Italy.
Rosalba
R
Giugno
Department of Mathematics and Computer Science, University of Catania, Città Universitaria, viale Andrea Doria 6, 95125, Catania, Italy. apulvirenti@dmi.unict.it.
Alfredo
A
Pulvirenti
D000465:Algorithms; D001702:Biopharmaceutics; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery; D004347:Drug Interactions; D058492:Drug Repositioning / Q000379:methods*; D006801:Humans; D008024:Ligands; D058990:Molecular Targeted Therapy
D008024:Ligands
Drug combination prediction; Drug repositioning; Drug–target interaction prediction; Hybrid methods network-based prediction; Recommendation systems