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Bioinformatics Advance Access published online on March 3, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti372
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received November 18, 2004
Revised January 24, 2005
Accepted March 1, 2005

Article

Prediction of subcellular localisation using sequence-biased recurrent networks

Mikael Bodén 1* and John Hawkins 1

1 School of Information Technology and Electrical Engineering, QLD 4072, The University of Queensland, Australia

* To whom correspondence should be addressed.
Mikael Bodén, E-mail: mikael{at}itee.uq.edu.au


  Abstract

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design, and reliable annotation of gene products. However, due to low similarity of such sequences and the dynamical nature of the sorting process, computational prediction of subcellular localisation of proteins is challenging.

Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6% and 5% on non-plant and plant data, respectively.

Availability: The Protein Prowler incorporating the recurrent network predictor described in this paper is available online at http://pprowler.imb.uq.edu.au/.


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