  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.
