کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7562937 | 1491532 | 2015 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Identifying protein arginine methylation sites using global features of protein sequence coupled with support vector machine optimized by particle swarm optimization algorithm
ترجمه فارسی عنوان
شناسایی پروتئین های متیلاسیون آرژینین با استفاده از ویژگی های جهانی پیوند پروتئینی همراه با دستگاه بردار پشتیبانی بهینه سازی شده توسط الگوریتم بهینه سازی ذرات
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
سایت های متیلاسیون آرژنین، ماشین بردار پشتیبانی، بهینه سازی ذرات ذرات، ویژگی های جهانی پیوند پروتئین،
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
Protein methylation, which plays vital roles in signal transduction and many cellular processes, is one of the most common protein post-translation modifications. Identification of methylation sites is very helpful for understanding the fundamental molecular mechanism of the methylation related biological processes. In silico predictions of methylation sites have emerged to be a powerful approach for methylation identifying. They also facilitate the performance of downstream characterizations and site-specific investigations. Herein, we proposed a novel strategy for the prediction of methylation sites based on a combination of the pseudo amino acid composition (PseAAC) and protein chain description as global features of protein sequence. The global features of protein sequence comprehensively utilize amino acid composition information and sequence-order information, along with the physicochemical properties and structural characteristics of amino acid information. Support vector machine (SVM) is invoked to build the prediction model for methylation sites on the basis of the global features of protein sequence. Meanwhile, a global stochastic optimization technique, particle swarm algorithm (PSO) is employed for effectively searching the optimal parameters in SVM. The prediction accuracy, sensitivity, specificity and Matthew's correlation coefficient values of the independent prediction set are 98.11%, 96.23%, 100% and 96.30%, respectively. It obviously indicates that our method has sufficient prediction effect in identification of the protein arginine methylation sites. As a comparison, other predictors are also constructed based on different feature extracting and modeling strategies. The results show that the proposed method can greatly improve the performance of arginine methylation sites prediction.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 146, 15 August 2015, Pages 102-107
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 146, 15 August 2015, Pages 102-107
نویسندگان
Yan Zhang, Lijuan Tang, Hongyan Zou, Qin Yang, Xinliang Yu, Jianhui Jiang, Hailong Wu, Ruqin Yu,