title: Improving translation initiation site and stop codon recognition by using more than two classes.
abstract: BACKGROUND
  The recognition of translation initiation sites and stop codons is a fundamental part of any gene recognition program. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. These methods all use two classes, one of positive instances and another one of negative instances that are constructed using sequences from the whole genome. However, the features of the negative sequences differ depending on the position of the negative samples in the gene. There are differences depending on whether they are from exons, introns, intergenic regions or any other functional part of the genome. Thus, the positive class is fairly homogeneous, as all its sequences come from the same part of the gene, but the negative class is composed of different instances. The classifier suffers from this problem. In this article, we propose the training of different classifiers with different negative, more homogeneous, classes and the combination of these classifiers for improved accuracy.
  
  
  RESULTS
  The proposed method achieves better accuracy than the best state-of-the-art method, both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. The method is tested on the whole human genome. The results for recognizing both translation initiation sites and stop codons indicated improvements in the rates of both false-negative results (FN) and false-positive results (FP). On an average, for translation initiation site recognition, the false-negative ratio was reduced by 30.2% and the FP ratio decreased by 10.9%. For stop codon prediction, FP were reduced by 41.4% and FN by 31.7%.
  
  
  METHODS
  The source code is licensed under the General Public License and is thus freely available. The datasets and source code can be obtained from http://cib.uco.es/site-recognition.
  
  
  BACKGROUND
  npedrajas@uco.es.
authors.affiliation: Department of Computing and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, Edificio Einstein, Planta 3, 14071 Córdoba, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Pérez-Rodríguez
authors.affiliation: Department of Computing and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, Edificio Einstein, Planta 3, 14071 Córdoba, Spain.
authors.forename: Alexis G
authors.initials: AG
authors.lastname: Arroyo-Peña
authors.affiliation: Department of Computing and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, Edificio Einstein, Planta 3, 14071 Córdoba, Spain.
authors.forename: Nicolás
authors.initials: N
authors.lastname: García-Pedrajas
mesh_terms: D001483:Base Sequence; D018387:Codon, Initiator; D018388:Codon, Terminator; D019295:Computational Biology / Q000379:methods*; D015894:Genome, Human; D006801:Humans; D014176:Protein Biosynthesis; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D060388:Support Vector Machine
chemical_list: D018387:Codon, Initiator; D018388:Codon, Terminator