![](/images/graphics-bg.png)
In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches
المؤلفون المشاركون
Liao, Zhijun
Huang, Yong
Yue, Xiaodong
Lu, Huijuan
Xuan, Ping
Ju, Ying
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-08-08
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1097060
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)