Paper Info
Title
Prediction of Outer Membrane Proteins by Support Vector Machines Using Combinations of Gapped Amino Acid Pair Compositions
Abstract
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.
Year
DOI
Venue
2005
10.1109/BIBE.2005.48
BIBE
Keywords
Field
DocType
support vector machines,om classifier,well-annotated subcellular localization,outer membrane proteins,gapped amino acid pair,psortb v,dipeptide composition,outer membrane protein,subcellular localization,globular protein,folding class,5-fold cross-validation,proteins,molecular biophysics,amino acid,membrane protein,support vector machine,cross validation
Cellular biophysics,Dipeptide,Biology,Amino acid,Globular protein,Support vector machine,Precision and recall,Molecular biophysics,Bioinformatics,Bacterial outer membrane
Conference
ISBN
Citations 
PageRank 
0-7695-2476-1
4
0.53
References 
Authors
12
5
Name
Order
Citations
PageRank
Ssu-Hua Huang161.61
Ru-Sheng Liu2717.81
Chien-Yu Chen336729.24
Ya-Ting Chao4222.55
Shu-Yuan Chen5797.23