A protein classification engine based on stochastic finite state automata

TL;DRAbstract

Accurate protein classification is one of the major challenges in modern bioinformatics. Motifs that exist in the protein chain can make such a classification possible. A plethora of algorithms to address this problem have been proposed by both the artificial intelligence and the pattern recognition communities. In this paper, a data mining methodology for classification rules induction in proposed. Initially, expert – based protein families are processed to create a new hybrid set of families. Then, a prefix tree acceptor is created from the motifs in the protein chains, and subsequently transformed into a stochastic finite state automaton using the ALERGIA algorithm. Finally, an algorithm is presented for the extraction of classification rules from the automaton.

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