title: Multi-class subcellular location prediction for bacterial proteins.
abstract: Two algorithms, based onBayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection.
authors.affiliation: The Jenner Institute, University of Oxford, Compton,Newbury, Berkshire, RG20 7NN, UK.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Taylor
authors.forename: Teresa K
authors.initials: TK
authors.lastname: Attwood
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower