title: Translation initiation start prediction in human cDNAs with high accuracy.
abstract: BACKGROUND
  Correct identification of the Translation Initiation Start (TIS) in cDNA sequences is an important issue for genome annotation. The aim of this work is to improve upon current methods and provide a performance guaranteed prediction.
  
  
  METHODS
  This is achieved by using two modules, one sensitive to the conserved motif and the other sensitive to the coding/non-coding potential around the start codon. Both modules are based on Artificial Neural Networks (ANNs). By applying the simplified method of the ribosome scanning model, the algorithm starts a linear search at the beginning of the coding ORF and stops once the combination of the two modules predicts a positive score.
  
  
  RESULTS
  According to the results of the test group, 94% of the TIS were correctly predicted. A confident decision is obtained through the use of the Las Vegas algorithm idea. The incorporation of this algorithm leads to a highly accurate recognition of the TIS in human cDNAs for 60% of the cases.
  
  
  BACKGROUND
  The program is available upon request from the author.
authors.affiliation: Metagen GmbH, Ihnestr.63, 14195 Berlin-Dahlem, Germany. agh@pcbi.upenn.edu
authors.forename: Artemis G
authors.initials: AG
authors.lastname: Hatzigeorgiou
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D018076:DNA, Complementary / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D016571:Neural Networks, Computer; D010442:Peptide Chain Initiation, Translational / Q000235:genetics*; D012333:RNA, Messenger / Q000235:genetics; D012984:Software
chemical_list: D018076:DNA, Complementary; D012333:RNA, Messenger