By Topic

Classification artificial neural systems for genome research

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Wu, C.H. ; Dept. of Math. & Comput. Sci., Texas Univ., Tyler, TX, USA ; Whitson, G.M. ; Hsiao, C.-T. ; Huang, C.-F.

A neural network classification method has been developed as an alternative approach to the search/organization problem of large modular databases. Two artificial neural systems have been implemented on a Cray for rapid protein/nucleic acid classification of unknown sequences. The system employs a n-gram hashing function for sequence encoding and modular backpropagation networks for classification. The protein system has achieved a 82 to 100% sensitivity at a speed that is about an order of magnitude faster than other search methods. With the rapid accumulation of sequences, the saving in time will become increasingly significant. The pilot nucleic acid system showed a 96% classification accuracy. The software tool would be valuable for the organization of molecular sequence databases and is generally applicable to any databases that are organized according to family relationships

Published in:

Supercomputing '92., Proceedings

Date of Conference:

16-20 Nov 1992