Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation

Li, Chun; Zhao, Jialing; Wang, Changzhong; Yao, Yuhua

To further assess the effectiveness of the porposed method, we conduct a series of experiments of identification of DNA-binding proteins on three datasets: DNASet, DNAeSet and DNAiSet. Among them, DNASet and DNAeSet serve as training datasets, while DNAiSet serves as an independent testing dataset. Support vector machine (SVM) is employed as the classifier, and R package 'e1071' v1.6-8 [44] is used to implement SVM. For a given set of binary-labeled training examples, SVM maps the input space into a higherdimensional space and seeks a hyperplane to separate the positive samples from the negative ones [25] . The optimal hyperplane maximizes the separation margin between the two classes of training data. The distance measurement between the data points in the high-dimensional space is defined by the kernel function. In this study, we use the radial basis function (RBF) kernel . This model involves two tunable parameters: the kernel width and the penalty parameter C. Prediction performance can be assessed using some quality indices including Accuracy (ACC), Sensitivity (Se), Specificity (Sp), Fmeasure (F1M) and Matthews correlation coefficient (MCC) [2, 4, 5, 25, 37, 45] :