Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

文献类型： 外文期刊

第一作者： Ma, Xin

作者： Ma, Xin;Wu, Jiansheng;Xue, Xiaoyun

作者机构：

期刊名称：COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE （ 影响因子：2.238； 五年影响因子：2.477 ）

ISSN： 1748-670X

年卷期： 2013 年

页码：

收录情况： SCI

摘要： DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes useDNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序