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Hybrid Attractor Cellular Automata (HACA) for Addressing Major Problems in Bioinformatics

Pokkuluri Kiran Sree, Inampudi Ramesh Babu and SSSN Usha Devi Nedunuri

NLCA has grown as potential classifier for addressing major problems in bioinformatics. Lot of bioinformatics problems like predicting the protein coding region, finding the promoter region, prediction of the structure of protein and many other problems in bioinformatics can be addressed through Cellular Automata. Even though there are some prediction techniques addressing these problems, the approximate accuracy level is very less. An automated procedure was proposed with HACA (Hybrid Attractor Cellular Automata) which can address all these problems. Extensive experiments are conducted for reporting the accuracy of the proposed tool. The average accuracy of HACA when tested with ENCODE, BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.

Abbreviations: Non Linear Cellular Automata(NLCA) ,Cellular Automata (CA), Hybrid Attractor Cellular Automata (HACA), Genetic Algorithm (GA)