Neural networks for prediction of nucleotide sequences by using genomic signals

Authors:
Paul Cristea;Valeri Mladenov;Rodica Tuduce;Georgi Tsenov;Simona Petrakieva
Affiliations:
Biomedical Engineering Center, Universitry "Politechnica" of Bucharest, Bucharest, Romania;Department of Theoretical Electrical Engineering, Faculty of Automatics, Technical University of Sofia, Sofia, Bulgaria;Biomedical Engineering Center, Universitry "Politechnica" of Bucharest, Bucharest, Romania;Department of Theoretical Electrical Engineering, Faculty of Automatics, Technical University of Sofia, Sofia, Bulgaria;Department of Theoretical Electrical Engineering, Faculty of Automatics, Technical University of Sofia, Sofia, Bulgaria
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2008

Citing 3
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Abstract

The conversion of symbolic sequences into complex genomic signals allows using signal processing methods for the handling and analysis of nucleotide sequences. This methodology reveals surprizing regularities, both locally and at a global scale, allowing us to predict nucleotides in a sequence, when knowing the preceding ones. Such experiments have a major biologic significance, as they explore the possibility and the efficiency of error correction in processes like replication, transcription and translation.