Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization is needed for large-scale genome analysis. In this paper, we introduce a novel subcellular prediction method combining boosting algorithm with probabilistic neural network algorithm. This new approach provided superior prediction performance compared with existing methods. The total prediction accuracy on Reinhardt and Hubbard's dataset reached up to 92.8% for prokaryotic protein sequences and 81.4% for eukaryotic protein sequences under 5-fold cross validation. On our new dataset, the total accuracy achieved 83.2%. This novel method provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementing method to other existing methods based on sorting signals.
|Cite as: Guo, J., Lin, Y. and Sun, Z. (2004). A Novel Method for Protein Subcellular Localization Based on Boosting and Probabilistic Neural Network. In Proc. Second Asia-Pacific Bioinformatics Conference (APBC2004), Dunedin, New Zealand. CRPIT, 29. Chen, Y.-P. P., Ed. ACS. 21-27. |
(local if available)