Research article summary:

Parallel cascade recognition of exon and intron DNA sequences.

Abstract Extract:
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, ... (Full abstract text below)

Published 2002Jan in Journal: Ann Biomed Eng (Language : eng)

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1. Ann Biomed Eng. 2002 Jan;30(1):129-40

Parallel cascade recognition of exon and intron DNA sequences.

Korenberg MJ, Lipson ED, Green JR, Solomon JE

Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada. korenber@post.queensu.ca

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 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. 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.

PMID : 11874136 [PubMed - Indexed for MEDLINE]

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Full Author Information

First Name	LastName	Initials
Michael J	Korenberg	MJ
Edward D	Lipson	ED
James R	Green	JR
Jerry E	Solomon	JE

Affiliation: Department of Electrical and Computer Engineering, Queen's University, Kingston, Ontario, Canada. korenber@post.queensu.ca

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