کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6371627 | 1318971 | 2009 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Predicting DNA- and RNA-binding proteins from sequences with kernel methods
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم کشاورزی و بیولوژیک (عمومی)
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چکیده انگلیسی
In this paper, support vector machines (SVMs) are applied to predict the nucleic-acid-binding proteins. We constructed two classifiers to differentiate DNA/RNA-binding proteins from non-nucleic-acid-binding proteins by using a conjoint triad feature which extract information directly from amino acids sequence of protein. Both self-consistency and jackknife tests show promising results on the protein datasets in which the sequences identity is less than 25%. In the self-consistency test, the predictive accuracy is 90.37% for DNA-binding proteins and 89.70% for RNA-binding proteins. In the jackknife test, the predictive accuracies are 78.93% and 76.75%, respectively. Comparison results show that our method is very competitive by outperforming other previously published sequence-based prediction methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Theoretical Biology - Volume 258, Issue 2, 21 May 2009, Pages 289-293
Journal: Journal of Theoretical Biology - Volume 258, Issue 2, 21 May 2009, Pages 289-293
نویسندگان
Xiaojian Shao, Yingjie Tian, Lingyun Wu, Yong Wang, Ling Jing, Naiyang Deng,