A comparison of sequence kernels for localization prediction of transmembrane proteins

Maetschke, S., Gallagher, M. and Boden, M. (2007). A comparison of sequence kernels for localization prediction of transmembrane proteins. In: D. Fogel, Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007). IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007), Honolulu, Hawaii, (367-372). 1-5 April 2007.


Author Maetschke, S.
Gallagher, M.
Boden, M.
Title of paper A comparison of sequence kernels for localization prediction of transmembrane proteins
Conference name IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007)
Conference location Honolulu, Hawaii
Conference dates 1-5 April 2007
Proceedings title Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007)
Journal name 2007 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE - Institute of Electrical Electronics Engineers Inc.
Publication Year 2007
Sub-type Fully published paper
ISBN 1-4244-0710-9
Editor D. Fogel
Start page 367
End page 372
Total pages 6
Collection year 2008
Language eng
Abstract/Summary We applied support vector machines to the prediction of the subcellular localization of transmembrane proteins, and compared the performance of different sequence kernels on this task. More specifically we measured prediction accuracy, computation time, number of kernel evaluations and number of support vectors for the spectrum, the full spectrum, the wildcard, the mismatch, the local-alignment and the residue-coupling kernel. The local-alignment achieved the highest prediction accuracy, with a Matthews correlation coefficient of 0.51, closely followed by the mismatch kernel. However, the local-alignment kernel was also the most time consuming kernel and seven times slower than the mismatch kernel. The spectrum kernel was the fastest kernel but linked to the highest number of support vectors and kernel evaluations. The residue-coupling kernel showed the lowest number of support vectors and kernel evaluations. No correlation between the number of support vectors and prediction accuracy could be observed. A localization predictor (TMPLoc) has been made available at http://pprowler.itee.uq.edu.au/TMPLoc
Subjects 280207 Pattern Recognition
700199 Computer software and services not elsewhere classified
E1
Keyword Subcellular localization
Sequence kernels
Support vector machines
Q-Index Code E1
Q-Index Status Confirmed Code

 
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