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. The degenerate nature of the genetic code prevents backtranslation of amino acids into codons with certainty. In consideration of the long-range correlations in DNA, a neural network approach may identify sequence patterns in coding regions that could be used to improve the accuracy of backtranslation.
Neural networks are able to form generalizations and can identify patterns with noisy data sets.
Neural networks have also been used to study the structure of the genetic code. This network was able to correlate the structure of the genetic code to measures of amino acid hydrophobicity. Search by signal consists in identifying specific sites, such as splice sites. This method suffers from a lack of reliability when variable signals delimit the regions of interest. The goal of the research reported here is to utilize the successful NN techniques to analyze and generalize codon usage in mRNA sequences beginning at the CDS start site.