EnDNA-Prot:identification of DNA-binding proteins by applying ensemble learning


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.

Publication DOI: https://doi.org/10.1155/2014/294279
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
?? 50811700Jl ??
Additional Information: 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.
Uncontrolled Keywords: Biochemistry, Genetics and Molecular Biology(all),Immunology and Microbiology(all)
Publication ISSN: 2314-6141
Last Modified: 10 Jun 2024 07:12
Date Deposited: 03 Jun 2015 14:40
Full Text Link: http://www.hind ... ri/2014/294279/
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2014-05-26
Authors: Xu, Ruifeng
Zhou, Jiyun
Liu, Bin
Yao, Lin
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Zou, Quan
Wang, Xiaolong



Version: Published Version

License: Creative Commons Attribution

Export / Share Citation


Additional statistics for this record