@article{(Open Science Index):https://publications.waset.org/pdf/7349, title = {Neural Network Based Determination of Splice Junctions by ROC Analysis}, author = {S. Makal and L. Ozyilmaz and S. Palavaroglu}, country = {}, institution = {}, abstract = {Gene, principal unit of inheritance, is an ordered sequence of nucleotides. The genes of eukaryotic organisms include alternating segments of exons and introns. The region of Deoxyribonucleic acid (DNA) within a gene containing instructions for coding a protein is called exon. On the other hand, non-coding regions called introns are another part of DNA that regulates gene expression by removing from the messenger Ribonucleic acid (RNA) in a splicing process. This paper proposes to determine splice junctions that are exon-intron boundaries by analyzing DNA sequences. A splice junction can be either exon-intron (EI) or intron exon (IE). Because of the popularity and compatibility of the artificial neural network (ANN) in genetic fields; various ANN models are applied in this research. Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression Neural Networks (GRNN) are used to analyze and detect the splice junctions of gene sequences. 10-fold cross validation is used to demonstrate the accuracy of networks. The real performances of these networks are found by applying Receiver Operating Characteristic (ROC) analysis.}, journal = {International Journal of Electronics and Communication Engineering}, volume = {2}, number = {7}, year = {2008}, pages = {1426 - 1428}, ee = {https://publications.waset.org/pdf/7349}, url = {https://publications.waset.org/vol/19}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 19, 2008}, }