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Parallel Cascade Recognition of Exon and Intron DNA Sequences
Authors:Michael J Korenberg  Edward D Lipson  James R Green  Jerry E Solomon
Institution:(1) Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, K7L 3N6, Canada;(2) Department of Physics, Syracuse University, 201 Physics Building, Syracuse, NY;(3) Center for Computational Biology, The Beckman Institute, California Institute of Technology, Pasadena, CA
Abstract:Many of the current procedures for detecting coding regions on human DNA sequences combine a number of individual techniques such as discriminant analysis and neural net methods. Recent papers have used techniques from nonlinear systems identification, in particular, parallel cascade identification (PCI), as one means for classifying protein sequences into their structure/function groups. In the present paper, PCI is used in a pilot study to distinguish exon (coding) from intron (noncoding; interspersed within genes) human DNA sequences. Only the first exon and first intron sequences with known boundaries in genomic DNA from the betaT-cell receptor locus were used for training. Then, the parallel cascade classifiers were able to achieve classification rates of about 89% on novel sequences in a test set, and averaged about 82% when results of a blind test were included. In testing over a much wider range of human nucleotide sequences, PCI classifiers averaged 83.6% correct classifications. These results indicate that parallel cascade classifiers may be useful components in future coding region detection programs. © 2002 Biomedical Engineering Society. PAC2002: 8715Cc, 8714Gg, 8715Aa
Keywords:Nonlinear systems  Identification  Exons  Introns  DNA sequences
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