title: Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features.
abstract: BACKGROUND
  The prediction of a protein's contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation.
  
  
  RESULTS
  The main part of the evaluation is the comparison against the sequence-based contact prediction methods from CASP9, where our method presented the best rank in five out of the six evaluated metrics. We also assess the impact of the size of the ensemble used in our predictor to show the trade-off between performance and training time of our method. Finally, we also study the rule sets generated by our machine learning system. From this analysis, we are able to estimate the contribution of the attributes in our representation and how these interact to derive contact predictions.
  
  
  BACKGROUND
  http://icos.cs.nott.ac.uk/servers/psp.html.
  
  
  BACKGROUND
  natalio.krasnogor@nottingham.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Interdisciplinary Computing and Complex Systems research group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.
authors: Jaume
authors: J
authors: Bacardit
authors: Paweł
authors: P
authors: Widera
authors: Alfonso
authors: A
authors: Márquez-Chamorro
authors: Federico
authors: F
authors: Divina
authors: Jesús S
authors: JS
authors: Aguilar-Ruiz
authors: Natalio
authors: N
authors: Krasnogor
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D053181:Caspase 8 / Q000737:chemistry; D053453:Caspase 9 / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins; C505530:CASP8 protein, human; C506433:CASP9 protein, human; D053181:Caspase 8; D053453:Caspase 9