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PMID: 17282998 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

NetPhosYeast: prediction of protein phosphorylation sites in yeast.

Bioinformatics (Oxford, England) ·Vol. 23 ·No. 7 ·2007-04-01 ·Pages 895-7

Ingrell CR, Miller ML, Jensen ON, Blom N

Abstract

We here present a neural network-based method for the prediction of protein phosphorylation sites in yeast--an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for an yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast. The NetPhosYeast prediction service is available as a public web server at http://www.cbs.dtu.dk/services/NetPhosYeast/.

MeSH Terms
Algorithms Binding Sites Fungal Proteins/chemistry,metabolism Neural Networks, Computer Pattern Recognition, Automated/methods Phosphorylation Protein Binding Sequence Analysis, Protein/methods Yeasts/metabolism
Chemicals
Fungal Proteins
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Ingrell Christian R
University of Southern Denmark, Campusvej 55, DK-5230, Odense M, Denmark.
Miller Martin L
Jensen Ole N
Blom Nikolaj
Article Info
Journal
Bioinformatics (Oxford, England)
Abbr.
Bioinformatics
ISSN
1367-4811
Published
2007-04-01
Epub
2007-00-05
Pages
895-7
Language
English
Region
England
NLM ID
9808944
Subset
IM
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