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Electronic Journal of Biotechnology
Universidad Católica de Valparaíso
ISSN: 0717-3458
Vol. 1, No. 3, 1998, pp. 196-201
Bioline Code: ej98023
Full paper language: English
Document type: Research Article
Document available free of charge

Electronic Journal of Biotechnology, Vol. 1, No. 3, 1998, pp. 196-201

 en Using a neural network to backtranslate amino acid sequences
White, Gilbert & Seffens, William


A neural network (NN) was trained on amino and nucleic acid sequences to test the NN's ability to predict a nucleic acid sequence given only an amino acid sequence. A multi-layer backpropagation network of one hidden layer with 5 to 9 neurons was used. Different network configurations were used with varying numbers of input neurons to represent amino acids, while a constant representation was used for the output layer representing nucleic acids. In the best-trained network, 93% of the overall bases, 85% of the degenerate bases, and 100% of the fixed bases were correctly predicted from randomly selected test sequences. The training set was composed of 60 human sequences in a window of 10 to 25 codons at the coding sequence start site. Different NN configurations involving the encoding of amino acids under increasing window sizes were evaluated to predict the behavior of the NN with a significantly larger training set. This genetic data analysis effort will assist in understanding human gene structure. Benefits include computational tools that could predict more reliably the backtranslation of amino acid sequences useful for Degenerate PCR cloning, and may assist the identification of human gene coding sequences (CDS) from open reading frames in DNA databases.

Amino acids, , Backtranslation, , Genetic code, Neural network, Nucleic acids

© 1998 by Universidad Católica de Valparaíso -- Chile
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