title: OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action.
abstract: BACKGROUND
  Several computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role.
  
  
  RESULTS
  OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role.
  
  
  METHODS
  The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Schroeder
authors.affiliation: Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain.
authors.forename: Carlota
authors.initials: C
authors.lastname: Rubio-Perez
authors.affiliation: Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Tamborero
authors.affiliation: Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain.
authors.forename: Abel
authors.initials: A
authors.lastname: Gonzalez-Perez
authors.affiliation: Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain.
authors.forename: Nuria
authors.initials: N
authors.lastname: Lopez-Bigas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016147:Genes, Tumor Suppressor; D023281:Genomics / Q000379:methods; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics; D009857:Oncogenes; D012984:Software