In terms of classifiers, the computational methods can be divided into template-based and machine-learning-based methods, depending on how they use the information from the putative DNA-binding proteins. Template-based methods can be further classified into two classes, one of which utilize a structural comparison protocol to detect significant structural similarity between the query and a template known to bind DNA at either the domain or the structural motif to assess the DNA-binding preference of the target sequence [11,12] and the other employ a sequence comparison protocol (such as PSI-BLAST) to detect significant sequence similarity between the query and a template known to bind DNA to evaluate the DNA-binding preference of the target sequence [13]. Machine-learning-based methods do not perform direct structural comparison, but typically follow a machine-learning framework. To obtain good predictive model, various machine-learning algorithms have been employed to construct classification models, such as support vector machine (SVM) [14-17], neural network [18-22], random forest [23], naïve Bayes classifier [24,25], nearest neighbor [26] and ensemble classifiers [27,28], [29]