Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics

Pokkuluri Kiran Sree; Inampudi Ramesh Babu; SSSN Usha Devi Nedunuri

Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics

Computational Engineering, Finance, and Science 2014-01-13 v1 Machine Learning

Authors: Pokkuluri Kiran Sree , Inampudi Ramesh Babu , SSSN Usha Devi Nedunuri

Abstract

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%.

Keywords

genetic algorithm sequence alignment principal component analysis

Cite

@article{arxiv.1310.4495,
  title  = {Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics},
  author = {Pokkuluri Kiran Sree and Inampudi Ramesh Babu and SSSN Usha Devi Nedunuri},
  journal= {arXiv preprint arXiv:1310.4495},
  year   = {2014}
}

Comments

arXiv admin note: text overlap with arXiv:1310.4342

Multiple Attractor Cellular Automata (MACA) for Addressing Major Problems in Bioinformatics

Abstract

Keywords

Cite

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