Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm
المؤلفون المشاركون
Zhang, Yuchao
Feng, Kaiyan
Zhang, Ning
Lu, Lin
Huang, Weiping
Huang, Guohua
Cai, Yu-Dong
Xu, Yaochen
Zhao, Jun
Li, Bi-Qing
المصدر
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-08-11
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Protein S-nitrosylation plays a very important role in a wide variety of cellular biological activities.
Hitherto, accurate prediction of S-nitrosylation sites is still of great challenge.
In this paper, we presented a framework to computationally predict S-nitrosylation sites based on kernel sparse representation classification and minimum Redundancy Maximum Relevance algorithm.
As much as 666 features derived from five categories of amino acid properties and one protein structure feature are used for numerical representation of proteins.
A total of 529 protein sequences collected from the open-access databases and published literatures are used to train and test our predictor.
Computational results show that our predictor achieves Matthews’ correlation coefficients of 0.1634 and 0.2919 for the training set and the testing set, respectively, which are better than those of k-nearest neighbor algorithm, random forest algorithm, and sparse representation classification algorithm.
The experimental results also indicate that 134 optimal features can better represent the peptides of protein S-nitrosylation than the original 666 redundant features.
Furthermore, we constructed an independent testing set of 113 protein sequences to evaluate the robustness of our predictor.
Experimental result showed that our predictor also yielded good performance on the independent testing set with Matthews’ correlation coefficients of 0.2239.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Guohua& Lu, Lin& Feng, Kaiyan& Zhao, Jun& Zhang, Yuchao& Xu, Yaochen…[et al.]. 2014. Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm. BioMed Research International،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-472340
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Guohua…[et al.]. Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm. BioMed Research International No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-472340
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Guohua& Lu, Lin& Feng, Kaiyan& Zhao, Jun& Zhang, Yuchao& Xu, Yaochen…[et al.]. Prediction of S-Nitrosylation Modification Sites Based on Kernel Sparse Representation Classification and mRMR Algorithm. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-472340
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-472340
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