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