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A Fast Multiple Attractor Cellular Automata with Modified Clonal Classifier for Coding Region Prediction in Human Genome

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Most of the problems in bioinformatics can be solved using many computer techniques. One of these techniques which can understand the logicality in various bioinformatics problems and address them efficiently is cellular automata. A class of CA which can handle fuzzy logic is employed with modified clonal algorithm to predict protein coding regions in human genome. The proposed classifier can handle any length DNA sequence and can handle huge datasets. This classifier is tested with Fickett and Tung datasets for length 54, 108 and 162. It is tested with datasets from Meenakshi Amman Medical College, Chennai for lengths 252 and 354. Five lakh sixty thousand one hundred and two (5, 60, 102) data components are trained/tested for obtaining the classifier accuracy which is more than 87.5% in an average. The average time to taken to predict the protein coding regions is lesser than 0.5 nano seconds for a DNA sequence of length 108. Specificity and sensitivity values are reported as 89.3 and 87.6 respectively.
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Keywords: CELLULAR AUTOMATA (CA); MODIFIED CLONAL CLASSIFIER (MCC); MULTIPLE ATTRACTOR CELLULAR AUTOMATA (MACA); PROTEIN CODING REGIONS

Document Type: Research Article

Publication date: June 1, 2014

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  • Journal of Bioinformatics and Intelligent Control (JBIC) is an international journal that publishes research articles in areas of the bioinformatics and intelligent control. JBIC is aimed to provide an international forum for the exchange of ideas and new scientific and technological findings to disseminate information and promote the transfer of knowledge between professionals in academia and industry. The journal publishes original research papers; review papers; technical reports and notes; short communications focused on emerging new developments in these research areas.
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