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.
| Original language | English |
|---|---|
| Pages (from-to) | 260-264 |
| Number of pages | 5 |
| Journal | Bioinformation |
| Volume | 1 |
| Issue number | 7 |
| Publication status | Published - 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.Keywords
- Bayesian network
- prediction method
- subcellular location
- membrane protein
- periplasmic protein
- secreted protein