Combining algorithms to predict bacterial protein sub-cellular location: parallel versus concurrent implementations

Paul D. Taylor, Teresa K. Attwood, Darren R. Flower

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)285-289
Number of pages5
Issue number8
Early online date5 Dec 2006
Publication statusPublished - 2006

Bibliographical note

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.


  • beta barrel transmembrane protein
  • prokaryotic membrane proteins
  • Bayesian networks
  • prediction method
  • subcellular location


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