کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
466288 697819 2016 17 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou's general PseAAC
ترجمه فارسی عنوان
پیش بینی رسپتورهای جفت شده با پروتئین G و خانواده فرعی آنها با ترکیب ویژگی های توالی مختلف در درون PseAAC عمومی چو
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کلمات کلیدی
رسپتورهای جفت شده با پروتئین G ؛ نزدیکترین همسایه موزون K؛ ارتباط حداکثر حداقل افزونگی ؛ خصوصیات مشتق توالی ؛ ضریب همبستگی متی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی


• In this paper the protein samples are represented by eight sequence derived properties.
• In this paper the amino acid composition, dipeptide composition, correlation features, composition, transition, distribution, sequence order descriptors and pseudo amino acid composition with total 1497 number of sequence derived features are used.
• Here a weighted k –nearest neighbor classifier are proposed to use with UNION of best 50 features selected by Fisher score based feature selection, ReliefF, FCBF, MRMR and SVM-RFE feature selection methods to exploit the advantages of these feature selection methods.

Background and objectiveThe G-protein coupled receptors are the largest superfamilies of membrane proteins and important targets for the drug design. G-protein coupled receptors are responsible for many physiochemical processes such as smell, taste, vision, neurotransmission, metabolism, cellular growth and immune response. So it is necessary to design a robust and efficient approach for the prediction of G-protein coupled receptors and their subfamilies.MethodsIn this paper, the protein samples are represented by amino acid composition, dipeptide composition, correlation features, composition, transition, distribution, sequence order descriptors and pseudo amino acid composition with total 1497 number of sequence derived features. To address the issue of efficient classification of G-protein coupled receptors and their subfamilies, we propose to use a weighted k-nearest neighbor classifier with UNION of best 50 features, selected by Fisher score based feature selection, ReliefF, fast correlation based filter, minimum redundancy maximum relevancy, and support vector machine based recursive elimination feature selection methods to exploit the advantages of these feature selection methods.ResultsThe proposed method achieved an overall accuracy of 99.9%, 98.3%, 95.4%, MCC values of 1.00, 0.98, 0.95, ROC area values of 1.00, 0.998, 0.996 and precision of 99.9%, 98.3% and 95.5% using 10-fold cross-validation to predict the G-protein coupled receptors and non-G-protein coupled receptors, subfamilies of G-protein coupled receptors, and subfamilies of class A G-protein coupled receptors, respectively.ConclusionsThe high accuracies, MCC, ROC area values, and precision values indicate that the proposed method is better for the prediction of G-protein coupled receptors families and their subfamilies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods and Programs in Biomedicine - Volume 134, October 2016, Pages 197–213
نویسندگان
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