G - Physics – 06 – F
Patent
G - Physics
06
F
G06F 19/20 (2011.01) G06F 19/24 (2011.01) C12Q 1/68 (2006.01) C40B 30/02 (2006.01)
Patent
CA 2325225
Many of the current procedures for detecting coding regions on human DNA sequences actually rely on a combination of a number of individual techniques such as discriminant analysis and neural net methods. A recent paper introduced the notion of using techniques from nonlinear systems identification as one means for classifying protein sequences into their structure/function groups. The particular technique employed, called parallel cascade identification, achieved sufficiently high correct classification rates to suggest it could be usefully combined with current protein classification schemes to enhance overall accuracy. In the present paper, parallel cascade identification is used in a pilot study to distinguish coding (exon) from noncoding (intron) human DNA sequences. Only the first exon, and first intron, sequence with known boundaries in genomic DNA from the .beta. T-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. These results indicate that parallel cascade classifiers may be useful components in future coding region detection programs. Key Words: Nonlinear Systems, Identification, Exons, Introns, DNA Sequences.
Bereskin & Parr Llp/s.e.n.c.r.l.,s.r.l.
Korenberg Michael J.
LandOfFree
Nonlinear system identification for class prediction in... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Nonlinear system identification for class prediction in..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonlinear system identification for class prediction in... will most certainly appreciate the feedback.
Profile ID: LFCA-PAI-O-1832356