Detecting and sorting targeting peptides wtih neural networks and support vector machines

Hawkins, John and Boden, Mikael (2006) Detecting and sorting targeting peptides wtih neural networks and support vector machines. Journal of Bioinformatics and Computational Biology, 4 1: 1-18. doi:10.1142/S0219720006001771


Author Hawkins, John
Boden, Mikael
Title Detecting and sorting targeting peptides wtih neural networks and support vector machines
Journal name Journal of Bioinformatics and Computational Biology   Check publisher's open access policy
ISSN 0219-7200
1757-6334
Publication date 2006-02
Sub-type Article (original research)
DOI 10.1142/S0219720006001771
Volume 4
Issue 1
Start page 1
End page 18
Total pages 18
Editor J. Wooley
M. Li
W. Limsoon
Place of publication London, United Kingdom
Publisher Imperial College Press
Collection year 2006
Language eng
Subject C1
280200 Artificial Intelligence and Signal and Image Processing
700103 Information processing services
Abstract This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).
Keyword protein subcellular localization
machine learning
neural network
recurrent neural network
support Vector machine
targeting peptide
amino acid sequence
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 10:36:24 EST