title: In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches.
abstract: Gamma-aminobutyric acid type-A receptors (GABAARs) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category prediction of GABAARs just from the protein amino acid sequence would be very helpful for the recognition and research of novel receptors. Based on the proteins' physicochemical properties, amino acids composition and position, a GABAAR classifier was first constructed using a 188-dimensional (188D) algorithm at 90% cd-hit identity and compared with pseudo-amino acid composition (PseAAC) and ProtrWeb web-based algorithms for human GABAAR proteins. Then, four classifiers including gradient boosting decision tree (GBDT), random forest (RF), a library for support vector machine (libSVM), and k-nearest neighbor (k-NN) were compared on the dataset at cd-hit 40% low identity. This work obtained the highest correctly classified rate at 96.8% and the highest specificity at 99.29%. But the values of sensitivity, accuracy, and Matthew's correlation coefficient were a little lower than those of PseAAC and ProtrWeb; GBDT and libSVM can make a little better performance than RF and k-NN at the second dataset. In conclusion, a GABAAR classifier was successfully constructed using only the protein sequence information.
authors: Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian 350122, China.
authors: Zhijun
authors: 0000-0002-7034-8657
authors: Z
authors: Liao
authors: College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471023, China.
authors: Yong
authors: Y
authors: Huang
authors: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
authors: Xiaodong
authors: 0000-0002-0536-1345
authors: X
authors: Yue
authors: College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China.
authors: Huijuan
authors: 0000-0002-3698-9653
authors: H
authors: Lu
authors: School of Computer Science and Technology, Heilongjiang University, Harbin, Heilongjiang 150080, China.
authors: Ping
authors: 0000-0001-5328-691X
authors: P
authors: Xuan
authors: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China.
authors: Ying
authors: Y
authors: Ju
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000596:Amino Acids / Q000737:chemistry; D001921:Brain / Q000378:metabolism; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D003657:Decision Making; D003663:Decision Trees; D006801:Humans; D008024:Ligands; D000069550:Machine Learning; D015233:Models, Statistical; D011963:Receptors, GABA-A / Q000737:chemistry*; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D008024:Ligands; D011963:Receptors, GABA-A