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BioMed Research International
Volume 2014, Article ID 294279, 10 pages
Research Article

enDNA-Prot: Identification of DNA-Binding Proteins by Applying Ensemble Learning

1School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
2Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
3Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 518055, China
4Gordon Life Science Institute, Belmont, Massachusetts, USA
5PKU-HKUST ShenZhen-Hong Kong Institution, Shenzhen, Guangdong 518055, China
6Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
7School of Engineering & Applied Science, Aston University, Birmingham B47ET, UK
8School of Information Science and Technology, Xiamen University, Xiamen, Fujian 316005, China

Received 28 February 2014; Revised 5 May 2014; Accepted 5 May 2014; Published 26 May 2014

Academic Editor: Dongchun Liang

Copyright © 2014 Ruifeng Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97–9.52% in ACC and 0.08–0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83–16.63% in terms of ACC and 0.02–0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public.