title: Drug-target interaction prediction through domain-tuned network-based inference.
abstract: BACKGROUND
  The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.
  
  
  RESULTS
  In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.
  
  
  BACKGROUND
  DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.
authors.affiliation: Department of Mathematics and Computer Science and Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Alaimo
authors.forename: Alfredo
authors.initials: A
authors.lastname: Pulvirenti
authors.forename: Rosalba
authors.initials: R
authors.lastname: Giugno
authors.forename: Alfredo
authors.initials: A
authors.lastname: Ferro
mesh_terms: D000465:Algorithms; D062313:Databases, Pharmaceutical; D055808:Drug Discovery; D017434:Protein Structure, Tertiary / Q000187:drug effects*; D011506:Proteins / Q000187:drug effects
chemical_list: D011506:Proteins