DocumentCode :
1373235
Title :
GRAIL: a multi-agent neural network system for gene identification
Author :
Xu, Ying ; Mural, Richard J. ; Einstein, J. Ralph ; Shah, Manesh B. ; Uberbacher, Edward C.
Author_Institution :
Inf. Group, Oak Ridge Nat. Lab., TN, USA
Volume :
84
Issue :
10
fYear :
1996
fDate :
10/1/1996 12:00:00 AM
Firstpage :
1544
Lastpage :
1552
Abstract :
Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system, called GRAIL. GRAIL is a multiple sensor-neural network-based system. It localizes genes in anonymous DNA sequence by recognizing features related to protein-coding regions and the boundaries of coding regions, and then combines the recognized features using a neural network system. Localized coding regions are then “optimally” parsed into a gene model. Through years of extensive testing GRAIL consistently achieves about 90% of coding portions of test genes with a false positive rate of about 10% A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA
Keywords :
DNA; biology computing; encoding; feature extraction; feedforward neural nets; genetics; medical computing; pattern classification; proteins; DNA sequences; GRAIL; feature recognition; feedforward neural networks; gene identification; multi-agent neural network; multiple sensor system; protein-coding; Algorithm design and analysis; Biology computing; DNA computing; Databases; Diseases; Genetics; Neural networks; Proteins; Sequences; Testing;
fLanguage :
English
Journal_Title :
Proceedings of the IEEE
Publisher :
ieee
ISSN :
0018-9219
Type :
jour
DOI :
10.1109/5.537117
Filename :
537117
Link To Document :
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