Improving signal peptide prediction accuracy by simulated neural network

I. Ladunga, F. Czakó, I. Csabai, T. Geszti

Research output: Contribution to journalComment/debate

32 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)485-487
Number of pages3
JournalBioinformatics
Volume7
Issue number4
DOIs
Publication statusPublished - Oct 1 1991

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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