newDNA-Prot: Prediction of DNA-binding proteins by employing support vector machine and a comprehensive sequence representation

作者：Zhang, Yanping; Xu, Jun; Zheng, Wei; Zhang, Chen; Qiu, Xingye; Chen, Ke^*; Ruan, Jishou

来源：Computational Biology and Chemistry, 2014, 52: 51-59.

DOI：10.1016/j.compbiolchem.2014.09.002

• DNA-binding proteins
• Features
• Feature selection methods
• SVM
• ROC

Identification of DNA-binding proteins is essential in studying cellular activities as the DNA-binding proteins play a pivotal role in gene regulation. In this study, we propose newDNA-Prot, a DNA-binding protein predictor that employs support vector machine classifier and a comprehensive feature representation. The sequence representation are categorized into 6 groups: primary sequence based, evolutionary profile based, predicted secondary structure based, predicted relative solvent accessibility based, physicochemical property based and biological function based features. The mRMR, wrapper and two-stage feature selection methods are employed for removing irrelevant features and reducing redundant features. Experiments demonstrate that the two-stage method performs better than the mRMR and wrapper methods. We also perform a statistical analysis on the selected features and results show that more than 95% of the selected features are statistically significant and they cover all 6 feature groups. The newDNA-Prot method is compared with several state of the art algorithms, including iDNA-Prot, DNAbinder and DNA-Prot. The results demonstrate that newDNA-Prot method outperforms the iDNA-Prot, DNAbinder and DNA-Prot methods. More specific, newDNA-Prot improves the runner-up method, DNA-Prot for around 10% on several evaluation measures.

• 出版日期2014-10
• 单位天津工业大学; 南开大学; 河北工程大学