@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},
	}