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In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches
Joint Authors
Liao, Zhijun
Huang, Yong
Yue, Xiaodong
Lu, Huijuan
Xuan, Ping
Ju, Ying
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-08
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Gamma-aminobutyric acid type-A receptors ( G A B A A R s) 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 G A B A A R s 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 G A B A A R 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 G A B A A R 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 G A B A A R classifier was successfully constructed using only the protein sequence information.
American Psychological Association (APA)
Liao, Zhijun& Huang, Yong& Yue, Xiaodong& Lu, Huijuan& Xuan, Ping& Ju, Ying. 2016. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches. BioMed Research International،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1097060
Modern Language Association (MLA)
Liao, Zhijun…[et al.]. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches. BioMed Research International No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1097060
American Medical Association (AMA)
Liao, Zhijun& Huang, Yong& Yue, Xiaodong& Lu, Huijuan& Xuan, Ping& Ju, Ying. In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1097060
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1097060