title: Improving signal peptide prediction accuracy by simulated neural network.
abstract: The accuracy of distinguishing amino-terminal signal peptides from cytosolic proteins has been improved to 95% by combining a neural network classifier with von Heijne's statistical prediction, the latter is itself 85-90% reliable. The network processed not the cleavage site, but amino-terminal 20-residue segments by the 'tiling' algorithm. Concordant positive predictions of both methods led to the safe identification of 496 novel signal peptides from the Protein Identification Resources.
authors.affiliation: Department of Genetics, Eötvös University, Budapest, Hungary.
authors.forename: I
authors.initials: I
authors.lastname: Ladunga
authors.forename: F
authors.initials: F
authors.lastname: Czakó
authors.forename: I
authors.initials: I
authors.lastname: Csabai
authors.forename: T
authors.initials: T
authors.lastname: Geszti
mesh_terms: D000595:Amino Acid Sequence; D016208:Databases, Factual; D004798:Enzymes / Q000235:genetics; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals / Q000235:genetics*; D011506:Proteins / Q000235:genetics*
chemical_list: D004798:Enzymes; D021382:Protein Sorting Signals; D011506:Proteins