Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to d...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Scientifica |
| Online Access: | http://dx.doi.org/10.1155/2016/8309253 |
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| _version_ | 1832546099736346624 |
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| author | Zhijun Liao Ying Ju Quan Zou |
| author_facet | Zhijun Liao Ying Ju Quan Zou |
| author_sort | Zhijun Liao |
| collection | DOAJ |
| description | G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs. In the testing datasets, the average accuracy was 91.61%, and the average AUC was 0.9282. MEME discovery analysis showed that many motifs aggregated in the seven hydrophobic helices transmembrane regions adapt to the characteristic of GPCRs. All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs. |
| format | Article |
| id | doaj-art-f43114ff67af4ceea8a6d6bf688ef138 |
| institution | Kabale University |
| issn | 2090-908X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Scientifica |
| spelling | doaj-art-f43114ff67af4ceea8a6d6bf688ef1382025-02-03T07:23:52ZengWileyScientifica2090-908X2016-01-01201610.1155/2016/83092538309253Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random ForestZhijun Liao0Ying Ju1Quan Zou2School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian 350108, ChinaSchool of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin 300350, ChinaG protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs. In the testing datasets, the average accuracy was 91.61%, and the average AUC was 0.9282. MEME discovery analysis showed that many motifs aggregated in the seven hydrophobic helices transmembrane regions adapt to the characteristic of GPCRs. All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.http://dx.doi.org/10.1155/2016/8309253 |
| spellingShingle | Zhijun Liao Ying Ju Quan Zou Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest Scientifica |
| title | Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest |
| title_full | Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest |
| title_fullStr | Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest |
| title_full_unstemmed | Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest |
| title_short | Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest |
| title_sort | prediction of g protein coupled receptors with svm prot features and random forest |
| url | http://dx.doi.org/10.1155/2016/8309253 |
| work_keys_str_mv | AT zhijunliao predictionofgproteincoupledreceptorswithsvmprotfeaturesandrandomforest AT yingju predictionofgproteincoupledreceptorswithsvmprotfeaturesandrandomforest AT quanzou predictionofgproteincoupledreceptorswithsvmprotfeaturesandrandomforest |