title: Gaussian interaction profile kernels for predicting drug-target interaction.
abstract: BACKGROUND
  The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy.
  
  
  RESULTS
  We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions.
  
  
  BACKGROUND
  Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/.
  
  
  BACKGROUND
  tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Computer Science, Radboud University Nijmegen, Nijmegen, The Netherlands. tvanlaarhoven@cs.ru.nl
authors: Twan
authors: T
authors: van Laarhoven
authors: Sander B
authors: SB
authors: Nabuurs
authors: Elena
authors: E
authors: Marchiori
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016503:Drug Delivery Systems; D055808:Drug Discovery / Q000379:methods*; D023281:Genomics