title: DIANA-EST: a statistical analysis.
abstract: BACKGROUND
  Expressed Sequence Tags (ESTs) are next to cDNA sequences as the most direct way to locate in silico the genes of the genome and determine their structure. Currently ESTs make up more than 60% of all the database entries. The goal of this work is the development of a new program called DNA Intelligent Analysis for ESTs (DIANA-EST) based on a combination of Artificial Neural Networks (ANN) and statistics for the characterization of the coding regions within ESTs and the reconstruction of the encoded protein.
  
  
  RESULTS
  89.7% of the nucleotides from an independent test set with 127 ESTs were predicted correctly as to whether they are coding or non coding.
  
  
  BACKGROUND
  The program is available upon request from the author.
  
  
  BACKGROUND
  Present address: Department of Genetics, University of Pennsylvania, School of Medicine, 475 Clinical Research Building, 415 Curie Boulevard, Philadelphia, PA 19104-6145, USA. artemis@pcbi.upenn.edu.
authors.affiliation: Metagen GmbH, Ihnestr.63, 14195 Berlin, Germany. artemis@pcbi.upenn.edu
authors.forename: A G
authors.initials: AG
authors.lastname: Hatzigeorgiou
authors.forename: P
authors.initials: P
authors.lastname: Fiziev
authors.forename: M
authors.initials: M
authors.lastname: Reczko
mesh_terms: D019295:Computational Biology; D016384:Consensus Sequence; D018076:DNA, Complementary / Q000235:genetics; D020224:Expressed Sequence Tags; D016571:Neural Networks, Computer; D017422:Sequence Analysis, DNA / Q000706:statistics & numerical data; D012984:Software
chemical_list: D018076:DNA, Complementary