Identification of functional sequences using associative memories

The identification and discrimination of functional sequences or mutations is very helpful in the medical area. Promoter and splice-junction identification, gene finding, DNA or Aminoacid database searching are some examples. Pattern recognition algorithms are candidates to perform this tasks. In this work we present a model, based on AlphaBeta associative memory and NeedlemanWunsch algorithm, to correctly recall altered version of learning patterns with one or more of the following modifications: insertions, deletions, and mutations, very common alterations in DNA and Aminoacid sequences. Moreover, this model preserve one of the most important advantages in associative memories, the correct recall of the fundamental set. To test the performance of the algorithm on bioinformatics and biomedical applications, the model presented here was tested using two datasets; one from the UCI repository; refered to promoter identification and the second one to using the genome of the Variovorax paradoxus organism obtained from the NCBI repository.