title: Predicting a DNA-binding protein using random forest with multiple mathematical features.
abstract: DNA-binding proteins are involved and play a crucial role in a lot of important biological processes. Hence, the identification of the DNA-binding proteins is a challenging and significant problem. In order to reveal the intrinsic information correlated to DNA-binding, nine classes of candidate features based on different mathematical fields are applied to construct the prediction model with random forest. They are fractal dimension, conjoint triad feature, Hilbert-Huang Transformation, amino acid composition, dipeptide composition, chaos game representation, and the corresponding information entropies. These mathematical expressions are evaluated with 5-fold cross validation test. The results of numerical simulations show that the mathematical features consisted of amino acid composition, fractal dimension and information entropies of amino acid and chaos game representation achieve the best performance. Its accuracy is 0.8157, and Matthew's correlation coefficient (MCC) achieves 0.5968 on the benchmark dataset from DNA-Prot. By analyzing the components of top combination of the nine candidate features, the concepts of fractal dimension and information entropy are the effective and vital features, which can provide complementary sequence-order information on the basis of amino acid composition.
authors.affiliation: College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, P.R. of China.
authors.forename: Changge
authors.initials: C
authors.lastname: Guan
authors.affiliation: College of Informatics, Huazhong Agricultural University, Wuhan,430070, P.R. of China.
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Niu
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan, 430070, P.R. of China.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.affiliation: College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, P.R. of China.
authors.forename: Kun
authors.initials: K
authors.lastname: Yang
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan, 430070, P.R. of China.
authors.forename: Nana
authors.initials: N
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry* / Q000235:genetics; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000235:genetics; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D004268:DNA-Binding Proteins; D004247:DNA
keywords: DNA-binding proteins; Hilbert-Huang transformation; fractal dimension; information entropy; random forest