Name
Abstract | ||
|---|---|---|
Automated annotation of G protein-coupled receptors (GPCRs) has been an intriguing topic because of the important role of GPCRs in pharmaceutical research. The diverse nature of GPCRs results in the lack of overall sequence homolog among members, making the classification of GPCRs a challenging task. In this paper, we propose a new method to classify GPCRs based on only their primary sequences. We extract feature vectors from protein sequences based on various physicochemical properties and use the Support Vector Machine (SVM) for the classification. When features derived from multiple properties are used together, we obtain the accuracy of 97.61% on GPCR Level I subfamily classification and 99.94% on GPCR superfamily recognition in double cross-validation tests. The results compare favorably with those reported in previous publications. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BMEI.2008.318 | BMEI (1) |
Keywords | Field | DocType |
g protein-coupled receptor,subfamily classification,challenging task,support vector machine,gpcrs result,multiple physicochemical properties,automated annotation,classifying g protein-coupled receptors,double cross-validation test,gpcr superfamily recognition,diverse nature,gpcr level,amino acids,feature vector,proteins,pharmaceuticals,g protein coupled receptors,cross validation,pharmaceutical research,gpcrs,svm,feature vectors,molecular biophysics,testing,biochemistry,protein engineering,biomedical engineering,g protein coupled receptor,feature extraction,polarization,protein sequence,support vector machines | Feature vector,SUPERFAMILY,Pattern recognition,G protein-coupled receptor,Computer science,Support vector machine,Sequence Homolog,Artificial intelligence,Subfamily | Conference |
ISSN | Citations | PageRank |
1948-2914 | 1 | 0.40 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyi Yang | 1 | 19 | 3.23 |
| Jitender S. Deogun | 2 | 1231 | 250.79 |