DocumentCode :
3316319
Title :
Functional site prediction on the DNA sequence by artificial neural networks
Author :
Hatzigeorgiou, Artemis ; Mache, Niels ; Reczko, Martin
Author_Institution :
Eur. Molecular Biol. Lab., Heidelberg, Germany
fYear :
1996
fDate :
4-5 Nov 1996
Firstpage :
12
Lastpage :
17
Abstract :
A modular system of neural networks is used to identify genes in DNA sequences of eukaryotic organisms. The identification task is decomposed into the detection of distinct signals using separate neural network modules. Such signals are coding regions, splice sites and transcription start regions (cap-site). A focus of the work is the use of back-percolation, cascade correlation, and time-delay neural networks. These give, in this particular application, better generalization than the well known backpropagation algorithm. This system achieves a prediction accuracy comparable to the traditionally designed gene identification packages and is able to produce more accurate protein sequences from the constructed gene structures
Keywords :
DNA; biology computing; molecular biophysics; neural nets; pattern recognition; sequences; DNA sequence; artificial neural networks; back-percolation; cap-site; cascade correlation; coding regions; eukaryotic organisms; functional site prediction; gene identification packages; neural network modules; protein sequences; splice sites; time-delay neural networks; transcription start regions; Accuracy; Artificial neural networks; Backpropagation algorithms; DNA; Neural networks; Organisms; Packaging; Sequences; Signal detection; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Systems, 1996., IEEE International Joint Symposia on
Conference_Location :
Rockville, MD
Print_ISBN :
0-8186-7728-7
Type :
conf
DOI :
10.1109/IJSIS.1996.565045
Filename :
565045
Link To Document :
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