title: ReLiance: a machine learning and literature-based prioritization of receptor--ligand pairings.
abstract: BACKGROUND
  The prediction of receptor-ligand pairings is an important area of research as intercellular communications are mediated by the successful interaction of these key proteins. As the exhaustive assaying of receptor-ligand pairs is impractical, a computational approach to predict pairings is necessary. We propose a workflow to carry out this interaction prediction task, using a text mining approach in conjunction with a state of the art prediction method, as well as a widely accessible and comprehensive dataset. Among several modern classifiers, random forests have been found to be the best at this prediction task. The training of this classifier was carried out using an experimentally validated dataset of Database of Ligand-Receptor Partners (DLRP) receptor-ligand pairs. New examples, co-cited with the training receptors and ligands, are then classified using the trained classifier. After applying our method, we find that we are able to successfully predict receptor-ligand pairs within the GPCR family with a balanced accuracy of 0.96. Upon further inspection, we find several supported interactions that were not present in the Database of Interacting Proteins (DIPdatabase). We have measured the balanced accuracy of our method resulting in high quality predictions stored in the available database ReLiance.
  
  
  BACKGROUND
  http://homes.esat.kuleuven.be/~bioiuser/ReLianceDB/index.php
  
  
  BACKGROUND
  yves.moreau@esat.kuleuven.be; ernesto.iacucci@gmail.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: IBBT Future Health Department/ESAT-SCD, KU Leuven, Kasteelpark Arenberg 10, 3001, Heverlee-Leuven, Belgium. ernesto.iacucci@gmail.com
authors.forename: Ernesto
authors.initials: E
authors.lastname: Iacucci
authors.forename: Léon-Charles
authors.initials: LC
authors.lastname: Tranchevent
authors.forename: Dusan
authors.initials: D
authors.lastname: Popovic
authors.forename: Georgios A
authors.initials: GA
authors.lastname: Pavlopoulos
authors.forename: Bart
authors.initials: B
authors.lastname: De Moor
authors.forename: Reinhard
authors.initials: R
authors.lastname: Schneider
authors.forename: Yves
authors.initials: Y
authors.lastname: Moreau
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D057225:Data Mining; D030562:Databases, Protein; D004285:Dogs; D006801:Humans; D008024:Ligands; D051379:Mice; D051381:Rats; D011956:Receptors, Cell Surface / Q000145:classification / Q000235:genetics / Q000378:metabolism*
chemical_list: D008024:Ligands; D011956:Receptors, Cell Surface