Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics
This work addresses bioinformatics problems like protein structure prediction, but it appears incremental as it builds on existing Cellular Automata methods with a new variant.
The authors tackled major bioinformatics problems such as predicting protein coding regions and promoter regions by proposing Multiple Attractor Cellular Automata (MACA), achieving an average accuracy of 78% across multiple datasets.
CA 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, predicting 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 MACA (Multiple Attractor Cellular Automata) which can address all these problems. The genetic algorithm is also used to find rules with good fitness values. Extensive experiments are conducted for reporting the accuracy of the proposed tool. The average accuracy of MACA when tested with ENCODE, BG570, HMR195, Fickett and Tongue, ASP67 datasets is 78%.