title: Predicting protein residue-residue contacts using deep networks and boosting.
abstract: BACKGROUND
  Protein residue-residue contacts continue to play a larger and larger role in protein tertiary structure modeling and evaluation. Yet, while the importance of contact information increases, the performance of sequence-based contact predictors has improved slowly. New approaches and methods are needed to spur further development and progress in the field.
  
  
  RESULTS
  Here we present DNCON, a new sequence-based residue-residue contact predictor using deep networks and boosting techniques. Making use of graphical processing units and CUDA parallel computing technology, we are able to train large boosted ensembles of residue-residue contact predictors achieving state-of-the-art performance.
  
  
  BACKGROUND
  The web server of the prediction method (DNCON) is available at http://iris.rnet.missouri.edu/dncon/.
  
  
  BACKGROUND
  chengji@missouri.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Jesse
authors.initials: J
authors.lastname: Eickholt
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D020407:Internet; D015233:Models, Statistical; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins