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Title |
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Combining algorithms to predict Bacterial protein sub-cellular location: Parallel versus concurrent implementations
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Authors |
Paul D. Taylor1, Teresa K. Attwood2 and Darren R. Flower1,*
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Affiliation |
1 The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; 2 Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
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darren.flower@jenner.ac.uk; * Corresponding author
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Phone |
+44 1635 577954 |
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Fax |
+44 1635 577908
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Article Type |
Prediction Model
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Date |
received November 24, 2006; accepted December 05, 2006; published online December 06, 2006
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Abstract |
We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.
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Keywords
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beta barrel transmembrane protein; prokaryotic membrane proteins; Bayesian Networks; prediction method; subcellular location |
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Citation |
Taylor et al., Bioinformation 1(8):285-289 (2006)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
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License |
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |