SVMtm: Support vector machines to predict transmembrane segments

Yuan, Z., Mattick, J. S. and Teasdale, R. D. (2004) SVMtm: Support vector machines to predict transmembrane segments. Journal of Computational Chemistry, 25 5: 632-636. doi:10.1002/jcc.10411


Author Yuan, Z.
Mattick, J. S.
Teasdale, R. D.
Title SVMtm: Support vector machines to predict transmembrane segments
Journal name Journal of Computational Chemistry   Check publisher's open access policy
ISSN 0192-8651
Publication date 2004-01-01
Sub-type Article (original research)
DOI 10.1002/jcc.10411
Volume 25
Issue 5
Start page 632
End page 636
Total pages 5
Place of publication Hoboken
Publisher John Wiley & Sons, Inc.
Language eng
Subject C1
270200 Genetics
780105 Biological sciences
Abstract A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.
Keyword Chemistry, Multidisciplinary
Svmtm
Transmembrane Helix Prediction
Location Of Transmembrane Segments
Coding Scheme
Membrane-protein Structure
Signal Peptides
Topology
Helices
Model
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 14:45:09 EST