title: enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.
abstract: 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.
authors: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors: Ruifeng
authors: R
authors: Xu
authors: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors: Jiyun
authors: J
authors: Zhou
authors: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 518055, China ; Gordon Life Science Institute, Belmont, Massachusetts, USA.
authors: Bin
authors: B
authors: Liu
authors: PKU-HKUST ShenZhen-Hong Kong Institution, Shenzhen, Guangdong 518055, China ; Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors: Lin
authors: L
authors: Yao
authors: School of Engineering & Applied Science, Aston University, Birmingham B47ET, UK.
authors: Yulan
authors: Y
authors: He
authors: School of Information Science and Technology, Xiamen University, Xiamen, Fujian 316005, China.
authors: Quan
authors: Q
authors: Zou
authors: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors: Xiaolong
authors: X
authors: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000737:chemistry*; D030562:Databases, Protein; D020407:Internet; D011237:Predictive Value of Tests; D011485:Protein Binding; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software
chemical_list: D004268:DNA-Binding Proteins; D020543:Proteome