Improving signal peptide prediction accuracy by simulated neural network

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

doi:10.1093/bioinformatics/7.4.485

Improving signal peptide prediction accuracy by simulated neural network

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

Statistics

Research output: Contribution to journal › Comment/debate › peer-review

35 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 language	English (US)
Pages (from-to)	485-487
Number of pages	3
Journal	Bioinformatics
Volume	7
Issue number	4
DOIs	https://doi.org/10.1093/bioinformatics/7.4.485
State	Published - Oct 1991

ASJC Scopus subject areas

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

Access to Document

10.1093/bioinformatics/7.4.485

Cite this

@article{1535e28093fd4b8abc5639cdf7d9213f,

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.",

author = "I. Ladunga and F. Czak{\'o} and I. Csabai and T. Geszti",

year = "1991",

month = oct,

doi = "10.1093/bioinformatics/7.4.485",

language = "English (US)",

volume = "7",

pages = "485--487",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "4",

}

TY - JOUR

T1 - Improving signal peptide prediction accuracy by simulated neural network

AU - Ladunga, I.

AU - Czakó, F.

AU - Csabai, I.

AU - Geszti, T.

PY - 1991/10

Y1 - 1991/10

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0025945726&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0025945726&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/7.4.485

DO - 10.1093/bioinformatics/7.4.485

M3 - Comment/debate

C2 - 1747780

AN - SCOPUS:0025945726

SN - 1367-4803

VL - 7

SP - 485

EP - 487

JO - Bioinformatics

JF - Bioinformatics

IS - 4

ER -

Improving signal peptide prediction accuracy by simulated neural network

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this