title: Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest.
abstract: 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.
authors.affiliation: School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian 350108, China; School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
authors.forename: Zhijun
authors.identifier: 0000-0002-7034-8657
authors.initials: Z
authors.lastname: Liao
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ju
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin 300350, China; State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300071, China.
authors.forename: Quan
authors.identifier: 0000-0001-6406-1142
authors.initials: Q
authors.lastname: Zou