Biological sequence prediction using General Fuzzy Automata

This paper shows how the newly developed paradigm of General Fuzzy Automata (GFA) can be used as a biological sequence predictor. We consider the positional correlations of amino acids in a protein family as the basic criteria for prediction and classification of unknown sequences. It will be shown how the GFA formalism can be used as an efficient tool for classification of protein sequences. The results show that this approach predicts the membership of an unknown sequence in a protein family better than profile Hidden Markov Models (HMMs) which are now a popular and putative approach in biological sequence analysis.