AIS-MACA- Z: MACA based Clonal Classifier for Splicing Site, Protein Coding and Promoter Region Identification in Eukaryotes

Pokkuluri Kiran Sree, Inampudi Ramesh Babu, SSSN Usha Devi N

Abstract


Bioinformatics incorporates information regarding biological data storage, accessing mechanisms and presentation of characteristics within this data. Most of the problems in bioinformatics and be addressed efficiently by computer techniques. This paper aims at building a classifier based on Multiple Attractor Cellular Automata (MACA) which uses fuzzy logic with version Z to predict splicing site, protein coding and promoter region identification in eukaryotes. It is strengthened with an artificial immune system technique (AIS), Clonal algorithm for choosing rules of best fitness. The proposed classifier can handle DNA sequences of lengths 54,108,162,252,354. This classifier gives the exact boundaries of both protein and promoter regions with an average accuracy of 90.6%. This classifier can predict the splicing site with 97% accuracy. This classifier was tested with 1, 97,000 data components which were taken from Fickett & Toung , EPDnew, and other sequences from a renowned medical university.


Keywords


MACA(Multiple Attractor Cellular Automata), CA(Cellular Automata), AIS (Artificial Immune System), Clonal Algorithm, AIS-MACA-Z(Artificial Immune System- Multiple Attractor Cellular Automata-Version Z)

Full Text:

PDF

References


Bevilacqua, Vitoantonio, Maurizio Triggiani et al. An expert system for an innovative discrimination tool of commercial table grapes. In Intelligent Computing Theories and Applications, Springer Berlin Heidelberg, 2012; 7390: 95–102p.

Ganguly, Niloy, Biplab K. Sikdar, et al. A survey on cellular automata. (2003).

Sarkar, Palash, and Subhamoy Maitra, Nonlinearity bounds and constructions of resilient Boolean functions. In Advances in Cryptology-CRYPTO 2000, Springer Berlin Heidelberg, 2000; 1880: 515–532p.

Maji, Pradipta, Chandrama Shaw et al. Theory and application of cellular automata for pattern classification. In Fundamenta Informaticae , 2003; 58(3): 321–354p.

Yin, Changchuan, and Stephen S-T. Yau, Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence. In Journal of theoretical biology, 2007;247(4): 687–694p.

Mena-Chalco, Jesús P., Helaine Carrer et al. Identification of protein coding regions using the modified Gabor-wavelet transform. In Computational Biology and Bioinformatics, IEEE/ACM Transactions on 2008; 5 (2): 198–207p.

Sree Pokkuluri Kiran , AIS-INMACA: A Novel Integrated MACA Based Clonal Classifier for Protein Coding and Promoter Region Prediction. In J Bioinfo Comp Genom, 2014; 1: 1–7p.

Nedunuri, SSSN Usha Devi, Inampudi Ramesh Babu, and Pokkuluri Kiran Sree, An Extensive Repot on Cellular Automata Based Artificial Immune System for Strengthening Automated Protein Prediction. In Advances in Biomedical Engineering Research, 2013;1(3): 1310–4342p.

Fickett, James W., and Chang-Shung Tung, Assessment of protein coding measures. In Nucleic acids research,1992; 20 (24): 6441–6450p.

Dreos, René, Giovanna Ambrosini et al. EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era. In Nucleic acids research, 2013; 41(D1): D157–D164p.


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/