DNA-binding proteins are a class of proteins which have a specific or general affinity to DNA and
include three important components: transcription factors; nucleases, and histones. DNA-binding
proteins also perform important roles in many types of cellular activities. In this paper we describe
machine learning systems for the prediction of DNA- binding proteins where a Support Vector Machine
and a Cascade Correlation Neural Network are optimized and then compared to determine the learning
algorithm that achieves the best prediction performance. The information used for classification is
derived from characteristics that include overall charge, patch size and amino acids composition. In total
121 DNA- binding proteins and 238 non-binding proteins are used to build and evaluate the system. For
SVM using the ANOVA Kernel with Jack-knife evaluation, an accuracy of 86.7% has been achieved with
91.1% for sensitivity and 85.3% for specificity. For CCNN optimized over the entire dataset with Jack
knife evaluation we report an accuracy of 75.4%, while the values of specificity and sensitivity achieved
were 72.3% and 82.6%, respectively.