ArticleBioMed research international2016
In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery.Frontiers in medicine · 2021Article
- Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China.Frontiers in cell and developmental biology · 2021Article
- Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost.BMC bioinformatics · 2020Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- Identification of DNA NEpigenetics & chromatin · 2020Article
- Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.Briefings in bioinformatics · 2019Article
- Bastion3: a two-layer ensemble predictor of type III secreted effectors.Bioinformatics (Oxford, England) · 2019Article
- Article
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6 authors.
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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.
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