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
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