g-MARS: Protein Classification Using Gapped Markov Chains and Support Vector Machines
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Classifying proteins using gapped Markov feature pairs
Neurocomputing
| Hi-index | 0.00 |
Discriminating outer membrane proteins from proteins with other subcellular localizations and with other folding classes are both important to predict further their functions and structures. In this paper, we propose a method for discriminating outer membrane proteins from other proteins by Support Vector Machines using combinations of gapped amino acid pair compositions. Using 5-fold cross-validation, the method achieves 95% precision and 92% recall on the dataset of proteins with well-annotated subcellular localizations, consisting of 471 outer membrane proteins and 1,120 other proteins. When applied on another dataset of 377 outer membrane proteins and 674 globular proteins belonging to four typical structural classes, the method reaches 96% precision and recall and correctly excludes 98% of the globular proteins. Our method outperforms the OM classifier of PSORTb v.2.0 and a method based on dipeptide composition.